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Outcomes Research: What It Is, What It's Not, and Why It's Important For FITs

February 15, 2017 | Dhaval Kolte, MD, PhD Education

The term "outcomes research" was first introduced in 1998 by Clancy and Eisenberg in their article in Science , which stated that "outcomes research – the study of the end results of health services that takes patients' experiences, preferences, and values into account – is intended to provide scientific evidence relating to decisions made by all who participate in health care." Realizing the importance and need for outcomes research in cardiovascular diseases, the National Heart, Lung, and Blood Institute of the National Institutes of Health convened a working group on 'Outcomes Research in CVD' in 2004 . The working group defined outcomes research as "applied clinical research that generates knowledge to improve clinical decision-making and health care delivery to optimize patient outcomes. It is the study of the delivery and consequences of health care on outcomes from the perspective of patients, providers, and the health care system."

While these two definitions elegantly capture the essence of what outcomes research is, there are still some common misunderstandings and misconceptions about this field. These have been previously addressed in much detail by Harlan M. Krumholz, MD, SM, FACC , in his article in Circulation: Cardiovascular Quality and Outcomes . I will briefly highlight two of these, which in my experience, are still the most common misconceptions about outcomes research among residents and FITs. First, outcomes research is not defined by its use of observational methodology and existing databases. Rather, it is defined by its objective or the very nature of the question it seeks to answer. Thus, outcomes research can utilize methodology of observational studies (retrospective or prospective), randomized trials, cost-effectiveness/economic analysis, surveys, or meta-analysis, whichever is most suited to the specific research question. Second, outcomes research is not a fad, but is now a well-established field that has grown exponentially over the past decade and a half. This is evident from the increasing number of centers for cardiovascular outcomes research across the country as well as internationally, some of which now even offer formal training programs in outcomes research.

So, why is it important for FITs to engage in cardiovascular outcomes research? As FITs and cardiologists we are trained to practice evidence-based medicine. However, despite our extensive knowledge of randomized controlled trials and best practices, we often lack basic information about contemporary patterns of care, the effectiveness of different clinical strategies in actual clinical practice (at the community, state, national and international level), and the barriers to delivering the best available care and achieving the maximal potential of various interventions. We need information about how to engage patients in decision making, the cost-effectiveness of treatments, and the impact of health care policy decisions on various patient outcomes.

Outcomes research can provide answers to most if not all of these questions. Outcomes research is not a single field, but rather allows you to work with experts across disciplines including basic/translational scientists, clinicians, epidemiologists and biostatisticians. It increases your knowledge and understanding of various research methodologies as mentioned before. Finally, at the individual or personal level, engaging in outcomes research also enables you to collaborate and share ideas with peers, and most importantly, seek mentors both at and outside your institution with whom you can develop life-long relationships. As someone once said, "If you want to be incrementally better: be competitive. If you want to be exponentially better: be cooperative."

This article was authored by Dhaval Kolte, MD, PhD , a Fellow in Training (FIT) at Brown University in Providence, RI, and the FIT Member of the ACC/AHA Task Force on Performance Measures.

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Outcomes research: What is it and why does it matter?

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Modern Methods of Clinical Investigation (1990)

Chapter: 4. what is outcomes research.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

4 What Is Outcomes Research? JOHN E. WENNBERG In "The Structure of Scientific Revolutions" Thomas Kuhn suggests that sci- ence is what scientists do. I want to take my cue from him and will discuss what our research team is doing that we call outcomes research. I then will return to Kuhn to make some more general observations about the status of out- comes research and the emergence of evaluative clinical sciences as a response to medicine's current predicament. WHICH RATE IS RIGHT? For some time, our research group has been conducting a series of investiga- tions of the outcomes of treatment for benign prostatic hyperplasia (BPH). These investigations are part of an effort to come to terms with the puzzling geographic variations in rates of use of medical care. By the mid 1970s, I had become convinced that these variations exist primarily because of differences in physician practice style, and that a resolution of the puzzle required direct inquiry into the effect on outcome of the various opinions or clinical theories underlying these practice styles (1~. By the early 1980s, Dan Hanley, David Soule, and Alice Russell had organized leading physicians in Maine into study groups to investigate the variations (2~. These study groups became the focus of an alliance between practicing physicians and researchers that made many of our studies possible. Of these studies, the assessments of alternative treatments for BPH have become the most widely known, and it is fair to say that it is in the groping for solutions to the problems associated with BPH that our understanding of out- comes research has developed the furthest (3-7~. The BPH study group, orga- 33

34 JOHN E. WENNBERG nized in 1982 by Robert Timothy, consists largely of practicing urologists. Our BPH assessment team consists of research urologists, medical care epidemiolo- gists, decision analysts, statisticians, psychometricians, computer scientists, and experts In interactive videodisc technology (see Table 4.11. At issue is the treatment of prostatism or obstruction of the urinary tract due to benign hyperplasia of the prostate gland. BPH is a very common condition, affecting the majority of men by the seventh or eighth decade of life. One com- TABLE 4.1 Members of the BPH Assessment Team Discipline Members Location Biostatistics Klim McPherson Oxford, U.K. Clinical Decision Analysis Michael J. Barry Albert G. Mulley Boston, MA Boston, MA Computer Science/Interactive Eric Baumgartner Hanover, NH Videodisc Technology J. Robert Beck Hanover, NH Joseph V. Henderson Washington, DC Harold C. Lyon, Jr. Hanover, NH Barbara Sasso Hanover, NH Coralea Wennberg Hanover, NH Medical Care Epidemiology/ Nicolas Black London, U.K. Claims Data Studies Thomas A. Bubolz Hanover, NH Marsha M. Cohen Manitoba, Canada Elliott S. Fisher Hanover, NH E. Robert Greenberg Hanover, NH David J. Malenka Hanover, NH Dale McLerran Hanover, NH Aviva Ron Tel Aviv, Israel Leslie L. Roos Manitoba, Canada Noralou P. Roos Manitoba, Canada John H. Wasson Hanover, NH John E. Wennberg Hanover, NH Medical Ethics Charles Culver Hanover, NH Psychometrics/ Tavs Folmer Anderson Copenhagen, Survey Research Denmark Floyd J. Fowler Boston, MA Urology Reginald Bruskewitz Madison, WI Abraham Cockett Rochester, NY John A. Heaney Hanover, NH H. Logan Holtgrewe Annapolis, MD Ernest Ramsey Manitoba, Canada Stuart M. Selikowitz White River Jet., VT Robert P. Timothy Portland, ME

WHAT IS OUTCOMES RESEARCH? 35 man treatment for BPH is an operation, a prostatectomy. The rate of prostatec- tomy varies strikingly among neighboring communities. In some places about 10 percent of men undergo this operation by age 85, while in other communities the proportion can be as high as 50 percent. In 1987, some 340,000 prostatec- tomies were performed for BPH at a cost of roughly $4 billion. Another com- mon treatment for BPH is watchful waiting. In communities with low rates of prostatectomy, proportionately more men with BPH are treated by this alterna- tive strategy. Clinical practice in these communities emphasizes the viewpoint that prostatectomy is an elective procedure, reserved for those with truly bur- densome symptoms. The assessment of BPH started with a discussion between the researchers and the members of the study group, who represented many geographic areas in Maine with considerable variations in rates of surgery. This discussion focused on establishing the different points of view or clinical theories behind these geo- graphic variations. Together with a review of the scientific papers published on BPH, it uncovered an important and unsettled controversy concerning the indi- cations for the operation. Many physicians hold the theory that prostatectomy should be performed early in the course of BPH as a preventive measure. They reason that if the operation is delayed, the patient will be older and at higher risk when it finally becomes unavoidable; if the operation is delayed, life expectancy is reduced. According to this theory, watchful waiting is not a rea- sonable option for most patients. Other physicians argue that the need for the operation is not inevitable, that it does not improve life expectancy for most patients and that the primary reason for surgery is relief of symptoms and improvement in the quality of life. According to this theory, watchful waiting is a reasonable option for patients who prefer to live with their symptoms to avoid the risks of surgery (4-6~. The assessment team tested this conflict in theory and reached several con- clusions. Evidence from the literature and from claims data demonstrated that the preventive theory was incorrect: an operation in patients with uncomplicat- ed BPH which most patients have very likely causes a slight decrease in life expectancy. The assessment thus confirmed the opinion of those physicians who felt the operation was justified because it reduces symptoms and improves the quality of life. Interview studies with patients before surgery and at three, six, and twelve months after surgery documented changes in symptom and func- tional status related to the operation. These studies showed that the value of the operation for most patients is that it is better than watchful waiting for reducing symptoms and improving quality of life. However, these gains are available only to patients willing to take the risks of the operation, which include death, failure to improve symptoms, impotence, and incontinence. The decision to undergo the operation is thus highly dependent on patients' preferences for out- comes and their attitudes toward risk. This substantial clarification of theory occurred without a prospective clini-

36 JOHN E. WENNBERG cat trial; the steps undertaken and the disciplines involved are listed in Table 4.2. Note the emphasis on study groups with physicians to learn about their the- ories of treatment, and focus groups with patients to learn about the expecta- tions and outcomes that matter to them. The literature review and the claims data provided enough information to define the actuarial expectations for the operated and unoperated patient (without chronic retention) and to test the pre- ventive theory of early prostatectomy. We had to depend on the placebo arm of a few drug trials and a scant series of four studies of BPH patients treated with watchful waiting for estimates of the natural history of symptoms and the fre- quency of crossover to operation. We found no satisfactory information on symptom relief after surgery in the literature, so we conducted our own study. The results provide convincing evidence that the operation is much more effec- tive than watchful waiting as a treatment for BPH symptoms. Thus, there is no dispute about the "main effect" superiority of surgery over watchful waiting. Decision analysis, used to test the preventive theory, also demonstrates that the decision to undergo the operation depends on patient attitudes toward impo- tence, incontinence, and the operative mortality rate. The subjective factors of risk aversion and personal tolerance for disease symptoms thus emerge as important elements of rational choice. The BPH assessment had immediate practical value for clinical decision making because it clarified controversies, established correct theory, and pro- viding detailed probability estimates, some of which had not been previously studied. The effect of practice style on variation in prostatectomy rates was traced to an incorrect belief in the preventive theory of early prostatectomy and TABLE 4.2 Synopsis of BPH assessment: Prostatectomy versus watchful waiting Steps Used in the BPH Assessment Evaluative Clinical Sciences: Methods/Disciplines Identify treatment theories and evaluate Identify and develop measures of relevant outcomes from patient's and from physician's points of view Undertake non-experimental studies to estimate (missing) outcomes probabilities Integrate information from all sources to test preventive theory and evaluate importance of patient utilities Structured review of the literature; meta-analysis; focus groups with . . . physicians Study focus groups; algorithms using claims data; patient interview instruments Claims data studies; interview studies of surgery patients Decision analysis used as "thought experiment"

WHAT IS OUTCOMES RESEARCH? 37 failure to take patient preferences into account when recommending prostatecto- my. The remedy for unwanted practice-style variations, we concluded, requires the active engagement of the patient in the decision. It requires informing physicians and patients of the risks and benefits of prostatectomy and its alter- native, watchful waiting. ENGAGING PATIENTS IN THE DECISION PROCESS We are seeking new ways to engage patients in the decision process and to make our detailed probability estimates available to patients and physicians in "real time" for use in clinical decision making. To do this, we have developed a "BPH Shared Medical Decision Making Procedure" based on interactive videodisc technology. This interactive program can be used in the physician's office after standardized information on health status and physical condition is entered into the data base. The patient is asked to provide information about symptoms, functional status, and the strength of his feelings about them. This information allows us to identify the relevant prognostic subgroup to which the patient belongs so that a patient can be given the best available estimate. The patient is then shown an audiovisual narration depicting the available choices, their possible outcomes, and associated probabilities. Interviews with two physician-patients (one who chose watchful waiting and the other prostatec- tomy) convey to the patient that there is indeed a choice; if physicians choose differently, so can their patients. Other interviews are testimonies about the principal outcomes, including an example of a complication associated with either choice. The interactive computer feature of the Shared Medical Decision Making Procedure means that information on the probabilities for outcomes is specific to the patient's subgroup, according to symptom severity and age. The patient can also exercise options to learn more about issues of particular con- cern and can review the presentation. At the end, the patient is given a printed synopsis to discuss with family and physician. The physician then helps the patient make a decision. The BPH assessment conducted so far does clarify controversies in basic clinical theory and provides better estimates than previously available for symp- tom status. But important uncertainties remain about some probabilities that are important for patients with BPH who are trying to evaluate risk. The attributable postoperative (30-day) risk of death for prostatectomy remains unclear. For patients who underwent surgery, the sample on which estimates of symptom improvement and incontinence are based is small (about 400), so the standard deviations are sometimes large. For untreated patients, the situation is even less satisfactory. Given a watchful waiting situation, we were unable to obtain an accurate estimate of the chance of a second episode of acute retention. This information is important for helping patients who present with acute reten- tion to decide on treatment. The characteristics of the watchful waiting sub-

38 JOHN E. WENNBERG group at risk for progression to chronic obstruction (with the potential for blad- der decompensation) are also unclear. The interactive videodisc program can help fill in these missing probability estimates because it is also a tool for outcomes research. At the first viewing, the patient can be enrolled in a prospective study of outcomes based on his choice of treatment a cohort study that we call a preference trial. For out- comes research, the patient must return later (e.g., after 3, 6, or 24 months), regardless of type of treatment, at which point additional information about health and satisfaction with choice is entered into the data base. These data, accumulated over time and for many patients, can be used by researchers to update the information presented to future patients. In this way, the device par- ticipates in its own update and helps improve the scientific basis of medicine. We also plan research protocols to improve our understanding of the "framing" effects of our data displays and to learn more about helping patients make deci- sions consistent with their preferences. An important objective of these research protocols is to learn more about, and improve the role of, the physician as counselor and "cognitive" advisor to patients. EXTENDING ASSESSMENT TO ALL RELEVANT TREATMENT THEORIES In the course of our work we have encountered a number of other theories or controversies concerning the treatment of BPH. One is an "old" theory that the transurethral approach to prostatectomy has better outcomes than the open prostatectomy. Another is that prazosin and other alpha-blockage drugs used to treat hypertension are useful treatments for symptoms of BPH. We also found new techniques for treating BPH based on balloon dilation and identified two promising drugs undergoing FDA evaluation, a less invasive operative tech- nique (prostatotomy), and a microwave diathermy treatment which is thought to relieve symptoms by reducing prostate size through the scarring of tissue. As our thinking about our role has evolved, we have come to see the advantages of having the assessment team include all alternative treatments, the old as well as the emerging. We therefore want to address the evaluative challenges each of these theories presents. With the exception of the relative effectiveness of transurethral prostatecto- my (TURP) versus open prostatectomy (an issue I will address below), these theories are quite new and few data exist on which to base an evaluation. The need for prospective evaluation thus arises. There is a debate within our group concerning approaches to prospective evaluation. We began with the orthodox view that a double-blind, placebo-controlled randomized clinical trial (RCT) is the optimal strategy for establishing the probability for outcomes of alternative treatments, but some of us are no longer sure that this is always the case. Our debate is not about the recognized difficulties of RCTs, such as the issue of

WHAT IS OUTCOMES RESEARCH? 39 placebo controls and patient blinding when surgery is involved, the problems of costs, logistics, and changing technology that can befuddle studies where end- points require many years of follow-up, or the problem posed by rare diseases where cases are few and far between. Our conclusions about the importance of patient preferences for rational decision making, at least in the treatment of BPH, raise questions about the role of randomization itself. The first question concerns the ethical requirements for shared decision mak- ing and the legal requirements for informed consent. When the outcomes of alternative treatments for a given condition are asymmetric in some significant dimension say, known differences in the risk of death patient preferences with regard to what is known about these risks should influence treatment assignments. This is a strong challenge for the planning of further studies of watchful waiting versus TURP, where the uncertainty is not even about the "main treatment effect" probabilities, but about prognosis in certain subgroups on the watchful waiting arm. When informed about this uncertainty, few patients who want to share responsibility for decision making are indifferent to randomization. In this case, the most efficient and ethical way to obtain the missing probabilities and characterize patient subgroups may well be a prefer- ence trial the systematic follow-up of patient cohorts where treatment assign- ments are made according to informed patient choice rather than by randomiza- tion. A similar problem may exist in the evaluation of drugs such as prazosin. In this situation, the "main effect" probabilities concerning symptom relief are unknown and the rationale for randomization seems more compelling. However, Phase I safety studies (and the vast experience gained with prazosin in its use as an antihypertensive drug) show that mortality, over the short term at least, is lower than that following surgery, and that the drug does not cause incontinence. Again, when informed, few patients who want to share responsi- bility for decision making accept randomization. The unwillingness of informed patients to accept randomization may not be the main point, however. The more important issue may be which approach provides the more useful information. To understand this, we need to know a good deal more about the confounding effects of study design on probability estimates, particularly for soft outcomes. A pragmatic view is that the informa- tion generated by clinical trials is useful only to the extent that it helps decide choice of treatment. When choice is delegated to the physician, the physician alone makes the connection between the evidence from clinical trials and the patient. In this case, the probabilities obtained under randomized designs that ignored or minimized the importance of patient preferences may be the best estimates for decisions. Under the shared decision making paradigm, however, it is not self-evident that classic RCTs provide the most accurate information. Imagine a clinical trial of prazosin in which patients with BPH are randomly assigned to a preference trial or an RCT. Patient characteristics (co-morbidities,

40 JOHN E. WENNBERG symptom status, demographics, etc.) are obtained before patients are given information about their choice. A modified version of the Shared Medical Decision Making Procedure (described above) is needed to present information uniformly to patients in all cooperating centers. On one arm of the trial, patients are offered surgery, watchful waiting, or the opportunity to participate in a dou- ble-blind, placebo-controlled randomized clinical trial of prazosin, based on the full disclosure of information which the ethics of shared patient decision mak- ing requires. On the other arm, they are offered a preference trial the opportu- nity to elect surgery, watchful waiting, or prazosin, based on the same disclo- sure about what is known (and not known) about the effect of the drug. Patients who want additional information or advice before deciding on treatment are counseled by physicians operating under standard protocols who do not admin- ister treatments. Follow-up to determine outcomes is by observers who are blinded as to treatment received. I have diagrammed such a trial in Figure 4.1. The trial would explore a number of interesting problems. Would more patients elect the drug if they knew they would get it than would elect the dou- ble-blind trial? Would the subgroup of patients electing the double-blind trial fairly represent those who elect prazosin with the knowledge that they will get their choice of treatment? That is, would the probabilities for symptom reduc- tion and other outcomes estimated in the double-blind trial be the same as those estimated in the preference trial? It seems to me they would not. Patients who agree to randomization after a fair presentation of the treatment dilemmas are very likely different from those who choose prazosin. Moreover, I suspect that the outcomes associated with freely chosen treatments will be more positive. SMDP-RCT Surgery N__/ C~ Placebo \ Watchful Waiting Surgery Prazosin \ Watchful Waiting FIGURE 4.1 Proposed trial of a trial: Preference design versus double-blind randomized design.

WHAT IS OUTCOMES RESEARCH? 41 The preference effect I am postulating is the common-sense notion that a treat- ment chosen by patients who believe it will help them is more likely to have a positive effect than randomly assigned treatments. Studies of the effect of patient compliance in double-blinded RCTs provide some strong hints about the importance of the preference effect. David Sackett has found five documented instances where patients who comply with treatment regimens have substantially better outcomes, including survival, than noncom- pliant patients, even on the placebo arm of the trial. Which set of probabilities is most relevant to the everyday clinical situation? Under the shared decision making paradigm, the answer seems intuitively clear. The correct probabilities are those based on preference trials, where patients knowingly choose treat- ments on the basis of information about probabilities for the outcomes relevant to them. However, reliance on preference trials makes sense only if we can dis- tinguish therapeutic effects from the effects of preference, placebo, and compli- ance. The trial I have proposed to you, because it also studies patients who choose watchful waiting and provides a complete accounting of choice and out- comes on both major arms of the study, may allow us to disentangle these effects. Under the paradigm of shared decision making, what has been viewed as a distinct boundary between clinical research and everyday clinical practice promises to become blurred. The tools that help patients make informed choice also are useful for organizing clinical studies. The preference trial approach may prove more acceptable to practicing physicians and increase the possibility for large-scale, cooperative studies. It is somewhat of a scandal that, although prostatectomies have been performed since the beginning of this census and more than 350,000 are now performed annually in the United States, fewer than 400 patients—our series in Maine- have been followed systematically to study the "main effect" outcome, the effect of the operation on symptom status. The information on symptom status and crossover to operation for patients choosing watchful waiting is based on only four studies of a small series of non-operated patients. I believe that many more patients will elect experimental treatments under preference designs than under randomized designs. If this is so, and if we become confident about the limitations and advantages of preference trials, this change will greatly speed the process and reduce the costs of technology assess- ment as well as provide new methods for Phase IV studies. For example, pra- zosin is now used extensively in everyday clinical practice in this country, but in order for its manufacturer to advocate its use for the treatment of BPH it must undergo an extensive series of RCTs. For all this trouble we would know little more than its relative value compared to a placebo; we would know nothing about the probabilities that count in clinical practice, such as its relative effec- tiveness compared to watchful waiting or surgery, and its value when patients freely choose their treatments. My hope is that our assessment team can broad- en the investigation of the relative advantages of preference versus randomized

42 JOHN E. WENNBERG clinical trials across the full spectrum of assessment issues we have encoun- tered, including the merits of balloon dilation, microwave diathermy, and sever- al new drugs. .. . ~ . .. . . . . · · . . we also need to consider the value or ootn trial Designs in resolving another treatment controversy: the relative effectiveness of two surgical approaches to the prostate the transurethral or the open prostatectomy. Over the past three decades, BURP has virtually replaced the older open operative techniques. This replacement, which occurred without prospective clinical trials, reflects the belief that TURP is a safer, less invasive operation that is effective in the long term. Since the administrative data bases available to our team extend back to the 1960s, we have compared the outcomes of these two operations over a con- siderable span of time. The results indicate that the open operation may be more effective. Patients with an open operation have significantly lower rates for stricture and reoperation, suggesting that the more complete removal of the prostate following the open operation, which is less traumatizing to the urethra, results in better long-term reduction of urinary symptoms. More puzzling and disturbing is the unexplained elevation in risk of death in the years following AMP. In separate studies undertaken in Maine (U.S.A.), Manitoba (Canada), Oxford (U.K.), and Denmark, we find the risk of death is significantly higher in the five years following operation. In view of our concern that the higher mortality following BURP may occur because those patients are sicker than those undergoing open prostatectomy, we abstracted data on severity of illness from the medical charts at a large universi- ty hospital. Even after adjustment, however, the 45 percent higher mortality rate following TURP was unchanged. The consistency of the findings, and our failure to explain them on the basis of data available in medical charts, led us to conclude that differences in co-morbidity do not explain the effect and that a prospective study is needed to evaluate this problem. Over the past several months, our assessment team has been in contact with urologists in various countries, including the leadership of the American Urological Association (AUA). We are extremely pleased that AUA has urged its members to participate in a large-scale clinical trial. The clinical trial being planned will not concentrate solely on the open versus TURP controversy. Since the available data suggest that BURP does harm, we have reasoned that this harm, if a true effect, will reveal itself no matter what the comparison group. The clinical trial thus provides the opportunity for direct comparison of new technologies balloon dilation, microwave diathermy, and the new drugs with surgery and watchful waiting. We hope that the clinical trial will provide the essential information on the outcome of all new treatment theories relevant to treatment of BPH. We emphasize randomization in this situation for several reasons. It would be unwise to base the primary test of open prostatectomies versus BURP on a preference trial, because the necessary methodologic work to understand the

WHAT IS OUTCOMES RESEARCH? limits and advantages of preference trials has not been done. The controversy here is about the role of patient risk factors as the confounding variables respon- sible for higher mortality rates following TURP. Well-conducted and large- scale randomized clinical trials give assurance that patient differences do not confound treatment effects. Moreover, it is ethical to randomize if the patient will accept randomization: our assessment team is uncertain whether the effect is caused by BURP or by differences in patient characteristics that we could not detect using the retrospective data available to us. The great majority of physi- cians do not believe the effect is due to the operation. As a secondary strategy, however, there may be an advantage if practices not able to participate in randomized clinical trials (because open operations are not available) participate in preference trials. Under the hypothesis that patient dif- ferences do not account for the observed differences in mortality following TURP versus open prostatectomy, TURP appears to reduce life expectancy. The effect should therefore be observable in a preference trial where BURP is compared to a control population undergoing watchful waiting or some other alternative. An advantage of such preference trials would be the speed with which additional information on this important problem could be expected to emerge. THE EVALUATIVE CLINICAL SCIENCES AND THE FUTURE OF MEDICINE I now turn to the more speculative task of attempting to relate outcomes research to current struggles about the future of medicine. Again, Thomas Kuhn provides an appropriate context. He teaches that when the rules and pro- cedures of a science no longer deal effectively with anomalies of theory or experimental evidence, a new science emerges. Problems are stated in new ways; new disciplines establish themselves; and new techniques provide solu- tions not previously possible. I believe that the emergence of the evaluative clinical sciences, as a set of disciplines distinct from biomedical science, is a response to the increasingly obvious anomalies of the model on which clinical and health care policy decision making has been based. That model is the more or less passive delegation of decision making to physicians who, to use Kenneth Arrow's term, act as rational agents for patients and society to assure that health resources are distributed optimally. The rational agency theory depends on two key assumptions about physicians. First, that by virtue of the strength of biomedical science and their own clinical experience, physicians know the prognosis for the various treatments available to a patient with a given condi- tion. Second, that physicians choose treatments that maximize patient utility, and that they can wisely choose the right treatment for the individual patient, disentangling their own preferences to deal with the problem of interpersonal differences in utility. This requires successful negotiation of E. B. White's 43

44 JOHN E. WENNBERG observation that one man's meat is another man's poison, in order to make vicarious judgments about which treatment has the highest expected value for any patient. The anomalies of rational agency theory are now evident to a widening audi- ence. It is increasingly apparent that the large investment in biomedical science has not resulted in a consensus among physicians on the correct way to practice medicine, nor has it resulted in orderly assessment of treatment theories. Quite the contrary, biomedical science, by virtue of its prodigious success, appears to have increased uncertainty and practice-style variations by offering an ever increasing supply of new technology and new biomedical ideas. This is behind the shocking variations in costs and utilization between Boston and New Haven, where the scientific credentials of local medicine are state of the art. It is also increasingly apparent that patients want to participate in determining their own medical fates and that physicians have neither the information nor the decision mak- ing skills to always choose the treatment that will maximize patient utility. These challenges, emerging at a time of runaway costs, growing consumerism, and concern about patients' rights, have effectively dissolved long held assump- tions about the efficacy, the ethical sufficiency, and the legal basis of the dele- gated decision making model. The breakdown is apparent in two ways. On the microscale, the impact is felt in the changing doctor-patient relationship, where physicians face an increasingly assertive and sometimes litigious clientele. The passive trust that manifested itself in the patient's willingness to delegate decision making to the physician and accept what happens as fate has receded. More and more, patients demand active involvement in the decisions that determine their medi- cal futures—even when this participation forces exposure to the reality that physicians do not know all the facts. This is not to say that patients must distrust their physicians or that a signifi- cant minority now do. But it does identify a new relationship based on an active patient role where decision making is shared, not delegated. In this new role, the challenge to the physician is to learn how to function in what some have called the cognitive role as an advisor or counselor who understands the need to act on imperfect information and who helps individual patients under- stand their own preferences, given the dilemmas the physician and the patient face together. It is perhaps on the macroscale of socioeconomics, of reaction to runaway costs, that the breakdown is most acutely felt by physicians. Understanding the flaws in the rational agency theory helps explain runaway costs. Demand is not controlled by professional consensus on "correct practice"; the supply of man- power and technologies, medical theory and practice style, are in dynamic equi- librium. Technologic possibilities, the numbers of physicians practicing medicine, and the costs of care are now such that many politicians, government officials, corporate officers, and labor leaders perceive further growth in the

WHATIS OUTCOMES RESEARCH? 45 health care sector as a threat to other societal priorities. More and more, gov- ernment and private corporations are intruding into the doctor-patient relation- ship in their attempts to control demand. The emergence of the evaluative clinical sciences and outcomes research is a response to the intellectual crisis created by the demise of the rational agency theory and the loss of faith that biomedical science, through its own internal logic, assures effective medical practice. It is an effort to provide the method- ologies, strategies, and knowledge base needed to support a new model for clin- ical decision making, one that Albert Mulley has suggested be called the shared clinical decision making model. The model preserves the notion that consumer preferences and knowledge about outcomes should serve as the regulator of patient behavior and of aggregate demand. But it also recognizes the inherent limits in the ability of physicians to determine vicariously what treatments patients value most. The evaluative clinical sciences seek to make possible bet- ter clinical decisions based on a fuller understanding of outcome probabilities, and active participation of patients in selecting treatments. The outcomes research agenda thus seeks to do something quite new. It focuses on the systematic evaluation of all of the outcomes that are relevant to patients mortality, morbidity, complications, symptom reduction, and func- tional status improvement as well as the physiologic or biochemical indicators which have perhaps too often been assumed to be valid surrogates for patient well-being. It focuses on the systematic evaluation of all (reasonably held) the- ories and alternative practice styles that are relevant to a particular condition or illness. It emphasizes assessment teams, organized around specific conditions with ongoing responsibility for evaluating all treatment options, old and new. The outcomes research agenda thus removes the "double standard of truth" that has characterized previous evaluation approaches. These approaches have main- ly concentrated on new drug evaluations, while ignoring evaluations of the use of approved drugs in novel ways, of surgical operations and other procedures such as balloon angioplasty, or of the use of hospitals compared to ambulatory care settings for chronic and acute conditions. Outcomes research focuses on the development of methods and strategies for conveying information in ways that activate the patient as a partner in decision making. The research agenda includes a broad emphasis on learning to help patients make decisions consis- tent with their own preferences. The outcomes research agenda focuses on new strategies and methods for making inferences to improve the validity and efficiency of evaluative research. Emphasis is placed on developing a proper balance between non-experimental and experimental methods for making inferences, and exploring the available options. Decision analysis is emphasized as a tool for organizing thinking, for conducting "thought experiments" to evaluate whether a particular treatment controversy is about the probabilities for outcomes, or whether it is really about the value of outcomes for patients. And, because the Shared Medical Decision

46 JOHN E. WENNBERG Making model emphasizes the importance of patient preferences, He agenda accentuates Be need to learn He advantages and limitations of basing treatment assignments in prospective clinical research protocols on patient preference rather than on randomization. REFERENCES 1. Wennberg JE, Bunker JP, Barnes B. The need for assessing the outcome of com- mon medical practices. Annual Review of Public Health 1980; 1 :277-295. 2. American Medical Association. Confronting Regional Variations: The Maine Approach. Chicago: American Medical Association, 1986. 3. Wennberg JE, Freeman JL, Shelton RM, Bubolz TA. Hospital use and mortality among Medicare beneficiaries in Boston and New Haven. New England Journal of Medicine 1989;321:1168-1173. 4. Wennberg JE, Mulley AG, Hanley D et al. An assessment of prostatectomy for benign urinary tract obstruction. Journal of the American Medical Association 1988;259:3027-3030. 5. Fowler FJ, Wennberg JE, Timothy RP et al. Symptom status and quality of life fol- lowing prostatectomy. Journal of the American Medical Association 1988;259:3018-3022. 6. Barry MJ, Mulley AG, Fowler FJ, Wennberg JE. Watchful waiting versus immedi- ate transurethral resection for symptomatic prostatism. Journal of the American Medical Association 1988;259:301~3017. 7. Wennberg J. Roos N. Sola L, Schori A, Jaffe R. Use of claims data systems to eval- uate health care outcomes: Mortality and re-operation following prostatectomy. Journal of the American Medical Association 1987;257:933-936.

The very rapid pace of advances in biomedical research promises us a wide range of new drugs, medical devices, and clinical procedures. The extent to which these discoveries will benefit the public, however, depends in large part on the methods we choose for developing and testing them.

Modern Methods of Clinical Investigation focuses on strategies for clinical evaluation and their role in uncovering the actual benefits and risks of medical innovation.

Essays explore differences in our current systems for evaluating drugs, medical devices, and clinical procedures; health insurance databases as a tool for assessing treatment outcomes; the role of the medical profession, the Food and Drug Administration, and industry in stimulating the use of evaluative methods; and more.

This book will be of special interest to policymakers, regulators, executives in the medical industry, clinical researchers, and physicians.

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Outcomes research articles from across Nature Portfolio

Outcomes research evaluates the quality and effectiveness of health care by measuring parameters such as mortality or quality of life. For example, it can provide evidence on the benefits and risks of particular drugs or other medical interventions in a real-world environment that can be used to improve overall treatment strategies.

Integrating traditional and modern medicines to treat heart failure

A large clinical trial supports the incorporation of qiliqiangxin, a traditional Chinese medicine, into standard treatment regimens for heart failure — highlighting a promising integration of ancient wisdom with modern medical practices through rigorous science.

• Zhuang Tian
• Shuyang Zhang

Latest Research and Reviews

Risk factors for mortality in hospitalized COVID-19 patients across five waves in Pakistan

• Nosheen Nasir
• Salma Tajuddin
• Adil H. Haider

Subphenotypes of platelet count trajectories in sepsis from multi-center ICU data

Impact of virtual simulation vs. Video refresher training on NRP simulation performance: a randomized controlled trial

• R. A. Umoren

Health and economic impacts of Lassa vaccination campaigns in West Africa

A modeling study quantifies the health-economic burden of Lassa virus infection across West Africa and projects impacts of a series of reactive and preventive vaccination campaigns against the disease, presenting substantial impacts in terms of averted disability-adjusted life years and healthcare cost reductions.

• David R. M. Smith
• Joanne Turner
• T. Déirdre Hollingsworth

Distribution and associated factors of keratometry and corneal astigmatism in an elderly population

• Hassan Hashemi
• Mohamadreza Aghamirsalim
• Mehdi Khabazkhoob

The association between dietary habits and self-care behavior of pregnant women with pregnancy complications

• Mehdi Karimi
• Maryam Mofidi Nejad
• Leila Azadbakht

News and Comment

Correspondence on: a model for anterior vitrectomy in real patients: simulation for practical training.

• Alasdair I. Simpson
• Jennifer Hind
• David Lockington

Comment on: “A nationwide study of breast reconstruction after mastectomy in patients with breast cancer receiving postmastectomy radiotherapy: comparison of complications according to radiotherapy fractionation and reconstruction procedures”

• Jilong Yuan

Preterm infants are not small term infants: Should the resuscitation of a <26-week preterm infant be initiated with 100% oxygen?

• Sylvia P. Bowditch
• Satyan Lakshminrusimha

Cochrane Corner: Anti-vascular endothelial growth factor biosimilar for macular degeneration

• Su-Hsun Liu
• Gianni Virgili

Comment on: ‘Short-term outcomes of Mitomycin-C augmented phaco-trabeculectomy using subconjunctival injections versus soaked sponges: a randomized controlled trial’

• Yuto Yoshida
• Shunsuke Taito
• Takashi Fujiwara

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Measurement, Design, and Analysis Methods for Health Outcomes Research

Related articles, the value of applying health outcomes research to improve treatment results, how health care executives can use health outcomes research for business decision making, using health outcomes research to improve quality of care, september 23 – 27, 2024.

This program is ideal for anyone interested in getting an intensive view of the fundamentals of health outcomes research.

• —Michael Loechel
• President & Co-Founder, Axioma Group

Program Overview

• Objectives & Highlights

Credits and Logistics

Who should participate.

Health care systems around the world face growing pressures to provide more effective programs, treatments, and interventions. Health outcomes research is the scientific inquiry that provides the evidence required for optimally developing new therapies, implementing quality of care improvement protocols, and enhancing public health system capacity and capability.

In this online program, Harvard’s expert faculty in health outcomes research will guide you through the language and concepts needed to synthesize outcomes studies into useful knowledge for translation and implementation into practice. During this program you will learn the fundamentals for measuring, designing, and analyzing health outcomes research studies and data.

Why Health Outcomes Research is Important

Outcomes research is highly valued in public health due to the breadth of issues it addresses. A diverse and growing number of organizations – employers, health care delivery organizations, insurers, pharmaceutical companies, and governments – rely on health outcomes research for generating actionable data that will help them make better decisions about the value of different therapeutic and preventative options. Well-designed and executed health outcomes research studies can provide evidence for a broad range of medical and public health practices, such as:

• Incorporating patient-centered outcomes measures such as patient preference and quality of life into the drug and device development process
• Evaluating the impact of chronic disease therapies on physical, emotional and social functioning
• Determining the cost-effectiveness of comparative medical treatments for chronic diseases
• Assessing the population health risks and benefits associated with response and mitigation strategies for infectious diseases such as influenza and COVID-19

Objectives & Highlights

Learning objectives.

As a participant, you will learn to:

• Conceptually define the meaning and purpose of outcomes research and evaluation
• Understand the role of epidemiology, health economics, psychometrics and biostatistics in conducting outcomes research
• Evaluate the usefulness of outcomes measures and the application of new technologies in medical institutions and industries, health care organizations and public health systems
• Recognize the different types of measures used in outcomes research, including clinical, health status, quality of life, work/role performance, health care utilization, and patient satisfaction
• Adopt new methods for modeling responses obtained from individuals completing questionnaires and surveys
• Interpret and understand scale performance and measurement concepts such as reliability, validity, responsiveness, and sensitivity
• Understand statistical methods appropriate for analyzing health outcomes data, such as longitudinal methods
• Synthesize and interpret findings from different types of health outcomes research studies

Continuing Education Credit

The Harvard T.H. Chan School of Public Health is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The Harvard T.H. Chan School of Public Health designates this live activity for a maximum of 16.25 AMA PRA Category 1 Credits ™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Harvard T.H. Chan School of Public Health will grant 1.6 Continuing Education Units (CEUs) for this program, equivalent to 16.25 contact hours of education. Participants can apply these contact hours toward other professional education accrediting organizations.

All credits subject to final agenda.

All participants will receive a Certificate of Participation upon completion of the program.

Current faculty, subject to change.

Donald C. Simonson, MPH, ScD, MD, MBA

Program director.

Marcia A. Testa, PhD, MPhil, MPH

Jakob b. bjørner, md, phd, garrett fitzmaurice, scd.

Matthias Rose, MD, PhD

Sebastian Schneeweiss, MD, ScD

Alexander turchin, ms, md, john e. ware jr., phd.

All Times are Eastern Time (ET).

This agenda is subject to change.

This program will be useful to anyone involved in improving health care cost, quality, or outcomes from across the health care industry. Ideal participants include:

• Clinicians, including physicians, nurses, and allied health professionals involved in utilizing outcomes data or assessing, measuring, and evaluating outcomes
• Health care executives and administrators who review and utilize outcomes data for clinical decision-making and improving quality of care
• Insurance, managed care, and other payer executives and researchers seeking to understand the relative value of procedures and interventions
• Pharmaceutical and biotechnology executives and researchers who need to measure patient-reported outcomes to support the benefit of their products
• Other health sector researchers who are new to outcomes research and need an overview of the latest information in the field

Registration Criteria

Participants must have an advanced degree (e.g., MD, PhD, MS, MPH, PharmD) in a relevant discipline. Proficiency in written and spoken English is essential for a successful learning experience in the program.

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What Is HEOR?

Interest in the field of health economics and outcomes research (HEOR) has grown exponentially as governments and other payers grapple with how to provide the best possible health outcomes at affordable costs. At ISPOR, we believe that every healthcare decision should be informed by the best scientific research derived from rigorous, proven methodologies. Toward that goal, the Society’s mission is to promote HEOR excellence to improve decision making for health globally.

In today's world, healthcare decision makers across the globe are often faced with the need to select therapeutic "interventions" from multiple treatment options, including biopharmaceuticals, medical devices, and healthcare services. However, the benefits and costs of these interventions can range dramatically and the benefits can be economic, clinical, both, or may include hard to measure costs or benefits the patient experiences directly. Health economics and outcomes research (HEOR) can help healthcare decision makers—including clinicians, governments, payers, health ministries, patients, and more—to adequately compare and choose among the available options.

HEOR Defined

What is health economics and outcomes research (heor).

Outcomes research comprises a set of scientific disciplines that evaluate the effect of healthcare interventions on patients.

Health economics and outcomes research (HEOR) is the confluence of 2 fields that work together to provide powerful data and insights for healthcare decision makers.

ISPOR—The Professional Society for Health Economics and Outcomes Research (HEOR) continues to monitor healthcare trends and conduct horizon scanning around the world. 

The “ISPOR 2024-2025 Top 10 HEOR Trends” marks the fifth publication of the Society’s biennial report, which is based on input from its members and strategic curation from its Health Science Policy Council .

View the Top 10 Trends

HEOR and ISPOR

During ISPOR’s 25+ year history, the Society has become the leading source for scientific conferences, peer-reviewed and MEDLINE ® -indexed publications, good practices guidance, education, collaboration, and tools/resources in the field of health economics and outcomes research. Learn more about how ISPOR contributes to the global HEOR community:

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ISPOR has defined the following HEOR topics in its taxonomy that are the key areas of focus for the organization and its membership:

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BIDMC’s Richard A. and Susan F. Smith Center for Outcomes Research Awarded $30 Million by PCORI for Large Study on Cardiac Implantable Electrical Devices

Funding awarded with guidance and consultations offered by harvard catalyst..

Harvard Catalyst regularly tracks funding opportunities from the Patient-Centered Outcomes Research Institute (PCORI) and shares that information with the Harvard biomedical research community. Our team provides resources and support to investigators as part of the application process, which happened recently with the following award .

Today, Beth Israel Deaconess Medical Center (BIDMC) announced a research team at the Richard A. and Susan F. Smith Center for Outcomes Research , in close partnership with Harvard T.H. Chan School of Public Health’s CAUSALab, has been approved for $30 million in research funding by the Patient-Centered Outcomes Research Institute ( PCORI ) for a large study on management strategies for patients with pacemakers and implantable cardioverter-defibrillators (ICDs).

This project, “ Remote Alert Pathway to Optimize CaRe of Cardiac Implantable Electrical Devices: RAPTOR-CIED ,” will be led by Daniel B. Kramer, MD, MPH, section head of electrophysiology and digital health at the Smith Center and associate professor of medicine at Harvard Medical School. Issa Dahabreh, MD, ScD, of Harvard Chan School’s CAUSALab and also of the Smith Center, will direct the analytic center for this study.

“The study has the potential to fill an important evidence gap relevant to a range of healthcare decision makers and help them better assess their care options.”

“This funding award aligns with the Smith Center’s mission to lead innovative research that improves clinical care and outcomes for cardiovascular patients,” said Kramer. “Modern pacemakers and ICDs collect and transmit enormous amounts of data to clinical sites. While this can be very helpful, we need better strategies for managing this deluge of information. Our project has the potential to change practice for a large and growing patient population with these devices.”

In this study, Kramer and colleagues will compare two strategies for managing patients living with pacemakers and ICDs. Current practice involves annual in-office device evaluations and wireless remote monitoring transmissions from patients’ devices, which include frequent scheduled transmissions, patient-initiated transmissions, and alert transmissions sent automatically. This standard strategy is effective, but imposes many burdens on patients related to time, cost, and complexity. An alternative strategy on remote monitoring alone with focus just on automatic alerts may be just as safe while reducing burdens on patients and clinical teams.

Collaborators from OpenNotes, the international movement of clinicians, patients and social scientists based at BIDMC that both encourages and evaluates transparent communication in healthcare, will work with Kramer and his team to explore patients’ and clinicians’ experiences throughout the study and analyze the data.

“This study was selected for PCORI funding based on its scientific merit and commitment to engaging patients in conducting a major research effort on cardiac electrophysiology,” said PCORI Executive Director Nakela L. Cook, MD, MPH. “The study has the potential to fill an important evidence gap relevant to a range of healthcare decision makers and help them better assess their care options. We look forward to following the study’s progress and working with the Smith Center to share its results.”

This study was selected for funding through a PCORI initiative to support large-scale, high-impact comparative effectiveness research trials in a multi-phase format allowing for testing and refinement of the study approach. The study will involve an initial feasibility phase to maximize the likelihood of full trial success. This project was selected through a highly competitive review process in which patients, caregivers, and other stakeholders joined scientists to evaluate the proposals.

This award has been approved pending completion of a business and programmatic review by PCORI staff and issuance of a formal award contract.

Originally published on August 13, 2024 in Beth Israel Deaconess Medical Center News .

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Research Implications | Definition, Examples & Tips

Introduction

What are research implications, why discuss research implications, types of implications in research, how do you present research implications.

Every scientific inquiry is built on previous studies and lays the groundwork for future research. The latter is where discussion of research implications lies. Researchers are expected not only to present what their findings suggest about the phenomenon being studied but also what the findings mean in a broader context.

In this article, we'll explore the nature of research implications as a means for contextualizing the findings of qualitative research and the foundation it sets for further research.

Research implications include any kind of discussion of what a particular study means for its research field and in general terms. Researchers write implications to lay out future research studies, make research recommendations based on proposed theoretical developments, and discuss practical and technological implications that can be applied in the real world.

To put it another way, research implications are intended to answer the question "what does this research mean?". Research implications look forward and out. Once findings are presented and discussed, the researcher lays out what the findings mean in a broader context and how they could guide subsequent research.

An aspect of academic writing that's related to implications is the discussion of the study's limitations. These limitations differ from implications in that they explore already acknowledged shortcomings in a study (e.g., a small sample size, an inherent weakness in a chosen methodological approach), but these limitations can also suggest how future research could address these shortcomings. Both the implications and recommendations are often coupled with limitations in a discussion section to explain the significance of the study's contributions to scientific knowledge.

Strictly speaking, there is a fine line between limitations and implications, one that a traditional approach to the scientific method may not adequately explore. Under the scientific method, the product of any research study addresses its research questions or confirms or challenges its expected outcomes. Fulfilling just this task, however, may overlook a more important step in the research process in terms of demonstrating significance.

One of the more famous research examples can provide useful insight. Galileo's experiments with falling objects allowed him to answer questions raised by Aristotle's understanding about gravity affecting objects of different weights. Galileo had something of a hypothesis - objects should fall at the same speed regardless of weight - based on a critique of then-current scientific knowledge - Aristotle's assertion about gravity - that he wanted to test in research. By conducting different experiments using inclines and pendulums (and supposedly one involving falling objects from the Tower of Pisa), he established a new understanding about gravity and its relationship (or lack thereof) to the weight of objects.

Discussion of that experiment focused on how the findings challenged Aristotle's understanding of physics. It did not, however, pose the next logical question: Why would an object like a feather fall at a much slower rate of descent than an object like a hammer if weight was not a factor?

Galileo's experiment and other similar experiments laid the groundwork for experiments on air resistance, most famously the Apollo 15 experiment on the moon where a feather and hammer fell at the same rate in a vacuum, absent any air resistance. The limitation Galileo had at the time was the inability to create a vacuum to test any theories about gravity and air resistance. The implications of his experiments testing Aristotle's claims include the call to further research that could eventually confirm or challenge his understanding of falling objects.

In formal scientific research, particularly in academic settings where peer review is an essential component, contemporary researchers are supposed to do more than simply report their findings. They are expected to engage in critical reflection in placing their research findings in a broader context. The peer review process in research publication often assesses the quality of a research paper by its ability to detail the significance of a given research study. Without an explicit description of the implications in research, readers may not necessarily know what importance the study and its findings holds for them.

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Download a free trial of our powerful analysis platform to generate critical insights from your research.

Breaking down the kinds of implications that your research findings might have will be useful in crafting a clearer and more persuasive presentation. More important than saying that the findings are compelling is arguing in what aspects the findings should prove useful.

There are different types of implications, and the type you should emphasize depends on your target audience.

Theoretical implications

When research findings present novel scientific knowledge, it should have an influence on existing theories by affirming, contradicting, or contextualizing them. This can mean the proposal of a brand new theoretical framework or developments to a existing one.

Keep in mind that, in qualitative research , researchers will often contextualize a theory rather than confirm or refute it. This means that a theory or conceptual framework that is applied to an unfamiliar context (e.g., a theory about adolescent development in a study involving graduate students) will undergo some sort of transformation due to the new analysis.

New understandings will likely develop more complex descriptions of theories as they are interpreted and re-interpreted in new contexts. The discussion of theoretical implications here requires researchers to consider how new theoretical developments might be applied to new data in future research.

Practical implications

More applied forums are interested in how a study's findings can be used in the real world. New developments in psychology could yield discussion of applications in psychiatry, while research in physics can lead to technological innovations in engineering and architecture. While some researchers focus on developing theory, others conduct research to generate actionable insights and tangible results for stakeholders.

Education research, for example, may present pathways to a new teaching method or assessment of learining outcomes. Theories about how students passively and actively develop expertise in subject-matter knowledge could eventually prompt scholars and practitioners to change existing pedagogies and materials that account for more novel understandings of teaching and learning.

Exploring the practical dimensions of research findings may touch on political implications such as policy recommendations, marketable technologies, or novel approaches to existing methods or processes. Discussion of implications along these lines is meant to promote further research and activity in the field to support these practical developments.

Methodological implications

Qualitative research methods are always under constant development and innovation. Moreover, applying research methods in new contexts or for novel research inquiries can lead to unanticipated results that might cause a researcher to reflect on and iterate on their methods of data collection and analysis .

Critical reflections on research methods are not meant to assert that the study was conducted without the necessary rigor . However, rigorous and transparent researchers are expected to argue that further iterations of the research that address any methodological gaps can only bolster the persuasiveness of the findings or generate richer insights.

There are many possible avenues for implications in terms of innovating on methodology. Does the nature of your interview questions change when interviewing certain populations? Should you change certain practices when collecting data in an ethnography to establish rapport with research participants ? How does the use of technology influence the collection and analysis of data?

All of these questions are worth discussing, with the answers providing useful guidance to those who want to base their own study design on yours. As a result, it's important to devote some space in your paper or presentation to how you conducted your study and what you would do in future iterations of your study to bolster its research rigor.

Presenting research implications or writing research implications in a research paper is a matter of answering the following question: Why should scholars read or pay attention to your research? Especially in the social sciences, the potential impact of a study is not always a foregone conclusion. In other words, to make the findings as insightful and persuasive to your audience as they are to you, you need to persuade them beyond the presentation of the analysis and the insights generated.

Here are a few main principles to achieve this task. In broad terms, they focus on what the findings mean to you, what it should mean to others, and what those impacts might mean in context.

Establish importance

Academic research writing tends to follow a structure that narrates a study from the researcher's motivation to conduct the research to why the research's findings matter. While there's seldom a strict requirement for sections in a paper or presentation, understanding commonly used patterns in academic writing will point out where the research implications are discussed.

If you look at a typical research paper abstract in a peer-reviewed journal , for example, you might find that the last sentence or two explicitly establishes why the research is useful to motivate readers to look at the paper more deeply. In the body of the paper, this is further explained in detail towards the end of the introduction and discussion sections and in the conclusion section. These areas are where you should focus on detailing the research implications and explaining how you perceive the impact of your study.

It's essential that you use these spaces to highlight why the findings matter to you. As mentioned earlier, this impact should never be assumed to be understood. Rather, you should explain in detail how your initial motivation to conduct the research has been satisfied and how you might use what you have learned from the research in theoretical and practical terms.

Tailor to your audience

Research is partly about sharing expertise and partly about understanding your audience. Scientific knowledge is generated through consensus, and the more that the researcher ensures their implications are understood by their audience, the more it will resonate in the field.

A good strategy for tailoring your research paper to a particular journal is to read its articles for the implications that are explored in the research. Applied journals will focus on more practical implications while more theoretical publications will emphasize theoretical or conceptual frameworks for other scholars to rely on. As a result, there's no need to detail every single possible implication from your study; simply describing those implications that are most relevant to your audience is often sufficient.

Provide useful examples

One of the easier ways to persuade readers of the potential implications of your research is to provide concrete examples that are simple to understand.

Think about a study that interviews children, for example, where the methodological implications dwell on establishing an emotional connection before collecting data. This might include practical considerations such as bringing toys or conducting the interview in a setting familiar to them like their classroom so they are comfortable during data collection. Explicitly detailing this example can guide scholars in useful takeaways for their research design.

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Outcomes research: an overview

Affiliation.

• 1 The Bobby R. Alford Department of Otorhinolaryngology and Communicative Sciences, Baylor College of Medicine, Houston, TX 77030, USA. [email protected]
• PMID: 15467339
• DOI: 10.1159/000079872

Outcomes research is increasingly important in contemporary medicine. Outcomes research differs from traditional clinical research in that outcomes are typically measured from the patient's perspective, and expanded measures of outcome are used, such as quality of life and cost-effectiveness. In this introduction to the special journal edition, the author will discuss the background behind outcomes research and describe several of its important aspects. Outcomes research consists of the clinical study of expanded, patient-based outcomes, as well as the study of populations, databases, and the delivery of health care. Outcomes research also includes the development of validated instruments to assess patient-based outcomes, development of disease severity staging systems, identification of comorbid conditions, and meta-analysis.

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Ending structural racism.

August 28, 2024

UNITE-Inspired Initiatives Promote Inclusive Excellence in Health Outcomes and Research Funding

UNITE Co-Chairs’ Corner

UNITE continues to be a driving force for change, working to ensure that equity is at the core of biomedical and behavioral science research. In this update, we highlight two UNITE-inspired initiatives, which are helping to shape a more inclusive scientific environment at NIH and beyond.

UNITE Structural Racism and Health Workshop

Led by the N Committee, UNITE  hosted a two-day  Structural Racism and Health Workshop in July, bringing together hundreds of researchers, clinicians, and community partners to share knowledge from their research and practice. Through presentations, panel discussions, and breakout sessions, participants explored determinants of adverse health outcomes and strategies to address the health impacts of structural racism where it may exist.

By fostering collaboration across disciplines and sectors, NIH can better advance understanding of minority health and health disparities and identify interventions that improve equity in health outcomes and research funding. We encourage you to listen to the workshop discussions: d ay one and day two videocasts are available.

The EARA Pilot Project

The NIH Engagement and Access for Research-Active Institutions (EARA) is a navigation and communication-focused initiative that enhances outreach and connections between Research-Active Institutions (RAIs) and NIH Institutes, Centers, and Offices.

Developed in response to a need identified by UNITE, EARA aims to address awareness and access barriers that RAIs face in enhancing research capacity and infrastructure, accelerating research progress, and addressing disparities in research opportunities and outcomes by connecting RAI faculty with NIH Institutes and Centers, Program Officers, and notices of funding opportunities.

A 2024–2025 EARA intensive engagement pilot project is now underway. It seeks to determine whether active information-sharing can accelerate engagement between RAIs and NIH, potentially leading to more grant applications and greater grant success for RAIs.

Approximately 50 RAIs volunteered to participate in the pilot project—far exceeding expectations. To accommodate this enthusiasm, the pilot was divided into three waves. The first wave, with 18 participating institutions, began in April. Wave 2 starts in September 2024 and Wave 3 is scheduled to start in early 2025.  NIH recently held a virtual gathering of participating RAI faculty and NIH leadership to discuss the project’s initial successes and ways to enhance its effectiveness. The event was also a valuable platform for networking and knowledge-sharing among participants.

If all three waves of the pilot project yield robust evidence suggesting a positive impact, NIH will explore making some form of intensive engagement a more generalized service.

Above and beyond the intensive pilot, resources that may be available for RAIs have been assembled on the EARA webpage . The webpage is designed with the intent to be a portal into NIH opportunities for all interested in advancing research at RAIs. This will be an evolving resource, and RAI faculty and other interested researchers are encouraged to visit the site regularly and sign up for the EARA Info Plus newsletter.

Your Ideas and Energy

A heartfelt thank you to everyone who has shared their ideas, energy, and passion to bring the Structural Racism and Health Workshop, EARA, and other UNITE initiatives to life. Your efforts and dedication have made UNITE a powerful catalyst for change within the biomedical and behavioral science research enterprise.

If you have been a regular follower of the UNITE Cochairs Corner, you will note that we have transitioned leadership. We are very grateful to Drs. Larry Tabak, Tara Schwetz, Alfred Johnson, and Noni Byrnes for their past leadership of UNITE. As we move forward into what we are envisioning as UNITE 2.0, we will be exploring other means of communicating UNITE developments. Thus, the UNITE Cochairs Corner may not come to your inbox in the future. However, you will hear from us by a variety of other means. If you have questions or concerns regarding UNITE developments, please do not hesitate to make outreach to [email protected] . And please regularly visit the UNITE webpage for the latest developments.

Authored by the UNITE Co-Chairs

Mohammed Aiyegbo, Ph.D.

Scientific Review Officer 

National Institute of Allergy and Infectious Diseases 

Camille M. Hoover, M.S.W.

Executive Officer 

National Institute of Diabetes and Digestive and Kidney Diseases

Marie A. Bernard, M.D.

Chief Officer for Scientific Workforce Diversity

NIH Office of the Director

This page last reviewed on August 30, 2024

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Methodological aspects of outcomes research

Rudi hiebert.

Department of Orthopaedic Surgery, Occupational and Industrial Orthopaedics Center (OIOC), NYU Hospital for Joint Diseases, New York University Medical Center, 63 Downing Street, New York, NY 10014 USA

Margareta Nordin

A critical evaluation of existing scientific evidence of treatment efficacy can be an important part of communicating risk and benefits of treatment options to patients during the course of clinical practice. A checklist of key methodological issues to examine when reading a research study is presented and discussed. Steps in reading a paper include: identifying the research question; identifying the manner in which subjects get enrolled in the study; identifying the treatments and outcomes used; identifying the study design and the comparisons being made; evaluating the study methods for the possibility of bias and uncontrolled confounding; assessing whether the statistical analysis used is appropriate for the study design; assessing whether the study has sufficient statistical power to demonstrate hypotheses being tested. Finally, procedures for grading and evaluating evidence, as used by systematic review groups and international best evidence synthesis consensus groups is briefly described.

Introduction

Outcomes research provides evidence of health care practices and interventions in terms of, for example, the ability to function, or the level of pain, the costs or the risks involved in undergoing a surgical procedure [ 18 ]. Outcomes research ties patient experience to the type of treatment they receive. In this way outcomes research serves as a powerful tool for improving the quality of care [ 18 ].

Drawing conclusions about the value of outcome studies can be daunting. Numerous studies are published, but few actually provide strong evidence for claims of treatment efficacy. For example, to formulate concensus findings regarding management of whiplash-associated disorders, the Quebec Task Force identified titles of over 10,000 whiplash-related articles and conference abstracts published over a 10 year period of time. Of these, 1204 studies met criteria for relevance, and only 294 met initial scientific quality criteria for further, in-depth review. Finally, only 62 studies were evaluated as having sufficient methodologic quality for acceptance into the Task Force recommendations. Consequently it is important to become a critical consumer of research information, to effectively judge whether findings from outcomes studies are trustworthy and relevant. This article is intended to help the reader to better understand the quality of an outcomes study, and how the findings of the study support the conclusions made.

Reading a study

Some systematic means for reading is needed to be able to identify those studies that provide useful information from those that provide information of no value or which are misleading. A list of steps can be a useful aid for efficiently reading research studies, one example is given in (Fig.  1 ).

Suggested checklist for evaluating methodologic aspects of outcomes research

Step 1: identify the research question

Evaluating outcome means asking the question whether one kind of treatment is better than another. What do we mean by better? Examples of outcomes research questions are in the spine surgery literature include: is patient satisfaction, quality of life, return to work, improved function and or pain with those getting one type of treatment as compared to another? Is pain reduced and mobility maintained? Is treatment cost-effective? Do the benefits of a particular treatment outweigh the risks?

A central methodological issue in evaluating outcomes research is whether the research question is worth investigating in the first place. Clinical research, particularly randomized controlled trials, involves subjecting patients to risks they would not otherwise normally encounter in seeking health care. For example, patients enrolled in clinical research are frequently asked to give information that does not pertain directly to decisions about their treatment, such as questions about depression, social activity, feelings about work, application for social compensation, and so on. These questions can be sensitive in nature and expose the patients to risks if disclosed to individuals or organizations that are not part of the research study. Second, patients participating in studies of new, experimental technologies undergo treatments and procedures where the risk of a serious adverse event is largely unknown. Finally, in some study designs, the treatment decision is not left up to the clinician and patient, but up to chance.

These aspects of participating in research fundamentally alter the normal patient–physician relationship. In routine clinical care the physician’s obligation is to provide scientifically validated treatment that best meets the patient’s needs. In clinical research, the obligation of the physician-scientist is to ensure that study participants are not subject to unreasonable risk for unnecessary or trivial reasons [ 39 ]. This means that the research question must have genuine medical, social and/or scientific value, that the research methods used give a good chance of obtaining findings that are trustworthy, and that the value and quality of the new knowledge gained offset risks to the subjects of participating in the study [ 39 ]. It also means that the research subject fully understands the nature of the changed doctor–patient relationship, the risk and benefits of the study, and voluntarily agrees to serve as research subject in a study.

Consequently, the first step a reader takes is to understand the purpose and merit of a study’s research question. A study’s literature review frames and justifies the research question being posed. A reader can evaluate the quality of a study’s literature review by looking for certain key features. For example, a good literature review should draw from multiple sources, both electronic and printed [ 34 ]. The keywords and search strategy used should be reported. The table of contents of journals specializing in the field can be hand searched, and international resources should be consulted when possible [ 6 ]. Finally, when possible experts in the field can be consulted to draw on their own personal libraries and experience to identify important, but hard-to-find studies. These techniques are used by international best evidence synthesis review groups [ 3 , 7 , 35 , 48 , 52 ] and systematic review groups [ 41 , 51 , 54 ].

Step 2: identify how subjects get enrolled in the study

The procedures by which a study enrolls potential study subjects helps the reader understand to what degree the findings in the study population can be generalized to other clinical populations. This is particularly important in studies of spine surgery because there may be disagreement among clinicians about the specific indications for surgical intervention. For example in a recent review, Carragee [ 15 ] found that there was little consensus among practioners regarding management of chronic, disabling low back pain. Some clinicians focus on identifying a specific anatomic feature thought to be responsible for the pain. However, in many cases the specific anatomic features thought to be ‘pain generators’ are also present in imaging studies of asymptomatic individuals [ 15 ]. Some researchers have suggested that the observed geographic variation in rates of spine surgery can be explained, in part, by local differences in what are considered the clinical indications for back surgery and clinical practice patterns [ 21 ].

For this reason it is essential that a good quality study report the exact enrollment procedures used by the study investigators. Ideally, a study protocol should not rely solely on the report of a diagnosis for purposes of inclusion or exclusion; instead, the clinical workup and diagnostic criteria need to be described sufficiently well so that a reader would be able to replicate the study’s enrollment procedures [ 40 ]. This may be difficult to do in retrospective studies because existing clinical records may only document the diagnosis and the specific procedures used for coming to the diagnosis and decision criteria may be incompletely or inconsistently documented. However, for prospective studies, particularly clinical trials, it is possible to employ uniform procedures for screening and enrollment, and these procedures should be described in the study’s publication.

In addition to using uniform clinical procedures to enroll subjects, a good quality study will also report results of the enrollment process. For example, the Consolidated Standards of Reporting Trials (CONSORT), an international group of biomedical journal editors and experts in clinical trial methodology, epidemiology and biostatistics, recommend that the following information be reported: the number of potentially eligible subjects from the study population, the number of subjects screened for inclusion/exclusion, the number of individuals eligible, and the number of subjects enrolling in the study [ 40 ]. If significant number of subjects drop out during the enrollment process (for example if a large number of subjects are screened as eligible but fail to enroll in the study), the reasons for the drop must be explored and reported in the study [ 40 ].

Step 3: identify the treatments being compared

The next step for the reader is to identify the treatments or interventions the study seeks to compare. The choice of comparisons is the central feature that confers clinical and scientific value to a study. What is an appropriate comparison group? There is no simple answer because the study questions reflect the interests, values and motivations of the clinical and research community to provide optimal care for patients. From a methodological standpoint, however, the treatments compared should be (1) distinguishable, (2) medically justifiable, (3) compatible with the needs of the patient, (4) have reasonable doubt regarding relative efficacy, (5) a mode of administration that is compatible with the methodological requirements of the study (for example, concealment is possible when blinding is needed), and (6) a mode of administration that is similar to real-world clinical practice [ 38 ].

It is essential that the treatments (both the experimental and control) are fully and completely described. For example, Brox et al. [ 12 ] compared fusion with conservative care among patients with chronic, non-specific low back pain. In this study, conservative care was defined as a cognitive-behavioral, multidisciplinary program. Brox et al. described the components of the program, the frequency of administration and the duration of treatment. On the other hand, Friztell et al. [ 28 ] evaluated fusion against conservative care. In this study, however, multiple, different modalities were used in the comparison group. Fritzell et al. did not capture data on the frequency, duration and type of treatment modalities used among those getting usual care. In this case, while the internal validity of the comparison was not substantially weakened, the lack of a clear description of the control group limits the generalizability of the study findings.

The issue of the use of a placebo in spine surgery research is hotly debated [ 21 , 24 ]. Some researchers argue that placebo is the only control group that permits a study to evaluate whether or not a new treatment is effective as compared to no treatment or treatment known to be ineffective. For example, when a treatment and control show no differences, one of two situations could have occurred. Both the treatment and control are effective, but equally so; or, both the treatment and control are ineffective [ 39 ]. Does the control group have to be a placebo or sham intervention? No, but the reader should understand that in those situations where no placebo or sham treatment control group is used, the best that the trial can do is make statements about relative efficacy, that is, whether one treatment does better than another in the outcomes presented, but cannot make statements regarding overall efficacy where no treatment would have been offered.

Step 4: identify the outcomes being assessed

When critically reading a study, the reader needs to ask three questions about the outcome measure reported being used. First, are the outcome measures meaningful? In the spine literature outcome measure fall into two different categories: patient-oriented outcomes and non-patient oriented outcomes. Patient-oriented outcomes reflect outcomes that are of importance to the patient, for example: pain, function, social and family life, ability to take care of oneself, ability to work and so on. Two of the most common and widely used patient-oriented outcome measures in spine literature include the Roland–Morris Disability Scale [ 44 ] and the Oswestry Disability Index [ 25 ] (see Table  1 ). Non-patient-oriented outcome measures reflect the interests of other stakeholders, such as the surgeon, the patient’s employer, social and health insurance organizations. For example, surgeons may be particularly interested in outcomes such as the stability of an implant, or the success of a fusion, or blood loss during surgery. On the other hand, social and health insurance organizations are interested in the cost benefit and cost-effectiveness of treatment. Importantly, there is a growing consensus in the surgical and research community that a single outcome measure is not sufficient. Investigators have called for using multiple outcome measures, such as a visual analogue scale in conjunction with the Oswestry and return to work status simultaneously [ 9 ]. Today, four domains of outcome are commonly recommended. They are: patient-oriented outcomes (for example, perceived pain, function and well-being), clinician-oriented outcomes (for example, fusion rate and blood loss in surgery), cost/health care utilization (for example, medical and disability costs) and societal outcomes (for example, return to and retention of work).

Table 1

Selected outcome measures used in studies of low back pain

Adapted and modified from Blount et al. (Blount KJ, Krompinger WJ, Maljanian R, Browner BD (2002) Moving toward a standard for spinal fusion outcomes assessment. J Spinal Disord Tech 15(1):16–23) and Bombardier et al. (Bombardier C (2000) Outcome assessments in the evaluation of treatment of spinal disorders: summary and general recommendations. Spine 25(24):3100–3103)

Second, are the outcome measures sensitive enough to detect important changes in the condition of the patient? There is a growing literature on establishing the ‘Minimally Clinically Important Difference’ (MCID), a criterion, which can be thought of as the difference in a specific measure of outcome that reflects a meaningful change in the health status of a patient. For example, for certain patient populations the MCID for the Oswestry Disability Index has been estimated by different observers between 4 and 17 points out of a 100 point scale [ 50 ]. What constitutes a MCID, however, is not necessarily fixed, and the MCID can (and should) vary depending on what perspective the investigator feels important [ 5 ].

Third, is the duration of follow-up sufficient? Although there is no fixed requirement for the duration of follow-up, investigators are calling for longer-term follow-up following surgical intervention to identify unexpected events related to the implant device. One study found an unexpected increase in the level of pain between 1 and 2 years following fusion [ 28 ]. The recently published United States Food and Drug Administration Investigational Device Exemptions (IDE) trials follow patients for 2 years after implant. As part of its recommendation of market approval of the SB Charité III artificial disc (June 2–3, 2004), the Food and Drug Administration’s Orthopaedic And Rehabilitation Devices Advisory Panel asked that the manufacturer continue follow-up of the IDE trial’s participants up to 5 years [ 2 ].

Step 5: identify the study design being used

Once the outcome and exposure measures are identified, the next question the reader needs to evaluate is: how are outcomes going to be compared between the various treatment groups? The study design embodies the procedures and analytic approach used to make the essential comparisons. We want to identify the study design for two reasons: (1) to anticipate what kind of statistical analysis will be used to analyze the data, and (2) to anticipate areas where bias and confounding may have been inadvertently introduced that would limit the strength of the conclusions being drawn from the findings. There are a finite number of clinical study designs. The major observational and experimental designs are discussed below.

Cross-sectional

A cross-sectional study can be thought of as a survey, where patients having a similar condition are identified and then characterized (for example by demographic features, the type of treatment they received, or by health status or outcome) (Fig.  2 ). Cross-sectional studies can provide information about prevalence and also information about associations between risk factors and outcome. For example, a recent cross-sectional study investigated the relationship between smoking, global health status and depression among spine patients in the United States. Patients from the National Spine Network ( n =25.455) were characterized according to smoking status (smoker or non-smoker), global health status (SF-36) and depression (Zung depression scale). The prevalence of smoking was 16% among the spine patients. The study found that smokers were more likely to report symptoms of depression than non-smokers (54 vs. 37% respectively), were more likely to report severe back symptoms (50 vs. 37% respectively) and scored lower on measures of overall physical and mental health [ 53 ].

Cross-sectional study design

Because the risk factor and the outcome are measured at the same time, cross-sectional studies cannot provide evidence that the exposure is causally related to the outcome. For example, data from Vogt et al. [ 53 ] suggest that smoking is related to depression among spine patients. But because the study is cross-sectional, it is not possible to tell whether individuals in the study began smoking and then became depressed, or whether they started to smoke because they felt depressed.

Case control

A study that can offer evidence for causality is the case control design (Fig. ​ (Fig.3). 3 ). The study design is retrospective in nature. It first identifies all those individuals with the outcome of interest (for example, all those exhibiting poor function documented by a high Oswestry score) called cases. It then collects a comparison group of individuals, called controls, who are similar in many respects to the cases, but who do not have the outcome of interest (for example, all those exhibiting good function documented by a low Oswestry score). Within each group a ratio, called an odds, is formed by comparing the number of cases exposed to a risk factor (for example, spinal surgery) to the number of cases not exposed to the risk factor. A similar odds is calculated for the controls. Estimate of association is calculated by forming the odds ratio, which is the ratio of the odds of exposure among the cases compared to the odds of exposure among the controls. When the odds ratio is at or around the value 1, it means the odds of exposure to the risk factor is about the same among the cases as it is among the controls. When the odds ratio deviates well away from the value of 1, it means that the odds of exposure in the cases is different from the odds of exposure among the controls. This is interpreted to mean that there is a statistical association between having the disorder and the likelihood of having been exposed to the risk factor.

Case control study design

Selecting appropriate controls to serve as a comparison group is a fundamental methodological issue in case control studies. Controls are those individuals that have had a chance of experiencing the exposure risk factor of interest (for example, surgery or conservative care), but are free of the outcome of interest (meaning they express no dysfunction). Controls meeting this definition can be drawn from any population; however, for reasons of generalizability it is desirable that the controls consist of a representative sample of the population from which they a drawn [ 45 ]. Matching is sometimes used to limit the influence of covariates on the findings of the study. Matching means pairing a case with a control on a certain characteristic, for example, age, gender or smoking. By doing so, the influence of these factors on the findings of the study are nullified. This makes it easier to detect statistically significant association between outcome and the exposure of interest. Care should be taken, however, not to over-match. Some authors recommend that matching not be done on more than three covariates [ 45 ]. Moreover, matching on the exposure variable of interest (for example, the kind of treatment a patient receives) makes it impossible in a case control study to assess the relationship between that treatment and outcome. Finally, a special statistical analysis (called a matched odds ratio) is required when analyzing data from a matched case control study [ 45 ].

Comparative and cohort study designs

A frequently used study design in the spine literature is a comparative study of outcome of patients drawn from a clinical practice or hospital. The design of this kind of study involves identifying a group of patients undergoing treatment for a specific disease. These patients are then classified according to the type of treatment they received. Outcome is then compared between the groups (Fig.  2 ).

Because of the possibility of uncontrolled confounding and lack of generalizability, simple comparative studies (case series) can provide only weak or suggestive evidence of treatment efficacy. However, because they are relatively inexpensive and easy to conduct, comparative studies are often used early in the development of new surgical procedures or devices. For example, early evaluative studies of artificial discs [ 13 , 17 , 19 , 23 , 32 , 47 , 56 ] identified clinical populations of individuals with chronic, discogenic low back pain eligible for spinal fusion. These early observational studies found that reduction in pain and improvement in function among those getting the artificial disc was no different from those getting spine fusion, but mobility of the spinal segment was largely maintained.

A form of a comparative study is the cohort study design (Fig. ​ (Fig.4). 4 ). The cohort study design is distinguished from the comparative study in that the number of individuals followed is larger and the catchment area is well described and representative. In epidemiologic studies, a cohort consists of an entire community of individuals sharing some common characteristic, for example living in the same geographic region or all sharing the same year of birth. In surgical studies, a cohort can be identified as all those individuals getting a particular experimental intervention for a specific condition (such as an artificial disc), or all those individuals who belong to a well defined community (such as members of a health maintenance organization, a geographical area or occupational group).

Cohort study design

The benefit of following an entire (or statistically representative) population of individuals is that a probability value, called risk, can be calculated for the cohort. Risk is defined as the proportion of individuals experiencing improved outcome among the entire cohort within a specific period of time. To evaluate treatment efficacy, relative risks are calculated by comparing the risk of improved outcome among those in the cohort obtaining one kind of treatment and comparing that to the risk of improved outcome among those getting an alternative treatment. A relative risk of 1 means the outcome experience is the same between groups getting different treatments. Relative risks diverging well away from 1 means the outcome experience between the comparison groups is different. Because an entire population is followed, the findings from a cohort study are far more generalizable than that of a simple comparative design (case series).

Randomized controlled trial

In an idealized experiment, identical samples are selected and exposed to various treatments. The logic behind this experiment is that, when all other factors that could be related to outcome are identical except for the type of treatment, then any difference in outcome is uniquely and solely attributable to the type of treatment used. Of all of the basic clinical research study designs, the randomized controlled trial comes the closest to this experimental ideal.

The key features of a randomized controlled trial are the following: (1) recruitment of potential subjects and screening for eligibility, (2) voluntary enrollment by means of informed consent, (3) screening to exclude those candidates for whom study participation would be contra-indicated, (4) experimental allocation by means of a random process, (5) pre-intervention assessment, (6) intervention, (7) post-intervention assessment, and (8) comparison of outcome between the study arms. To assess treatment efficacy, outcome among those in the intervention arm is compared with outcome among those in the control arm at the end of the study (Fig. ​ (Fig.5). 5 ).

The statistical analysis in a randomized clinical trial computes rates of outcome between the treatment groups. When the outcome is continuous in nature (for example, the Oswestry score, which is a index scale from 0 to 100) difference in outcome can be assessed using analysis of variance statistical method. When the outcome is categorical in nature (for example, the proportion of individuals having ‘excellent’ outcome following surgery) can be compared by calculating rate ratios. The interpretation of a rate ratio, as used in a clinical trial, is the same as that for a relative risk. A rate ratio of 1 means no difference in outcome between treatment groups; rate ratios that diverge well away from 1 mean the outcome experience between the treatment groups is different.

Step 6: evaluate the methods used to control for bias and confounding

Confounding is a factor that is associated with both with treatment and outcome [ 45 ]. Confounding can mask the observed statistical association between treatment and outcome, either inflating the estimate or diminishing it. For example, in a study comparing opioid patient controlled analgesia with non-steroidal analgesia (NSAIDs) following lumbar fusion surgery, Park et al. found that individuals infused with a combination of ketorolac and fentanyl were more likely to have delayed fusion than those using fentanyl alone, and concluded that NSAIDs were responsible for the delayed recovery [ 43 ]. However, in Park et al. study, individuals using NSAIDs were far more likely to smoke cigarettes than those taking opioids. Smoking is thought to interfere with osteogenesis. Since smoking in the study was associated both with the treatment (opioid versus NSAID) and also outcome (rate of fusion), smoking confounded the observed association between analgesia and fusion.

Bias is the systematic misrepresentation of rates or associations observed in a study [ 45 ]. Bias can be introduced in the way subjects are recruited, data are collected, or by using an incorrect statistical analysis procedure for the study design used [ 45 ]. The common methodological tools used to control for bias and confounding include: blinding, control of cross-over, randomization, control of differential follow-up, and correct statistical analysis.

Blinding refers to whether or not the allocation status of a study participant is known or not [ 38 ]. Blinding takes place at several levels. For example, a research subject is ‘blinded’ when that person does not know which treatment (the experimental or control) they are receiving. An independent evaluator, used to obtain measures of outcome during follow-up, is ‘blinded’ if that person does not know what kind of treatment the subject has received. Finally, the treating physician/clinician is ‘blinded’ if that person does not know the kind of intervention a particular research subject is allocated to receive. The terms ‘single’, ‘double’ and ‘triple’ blinding have been used to describe concealment of treatment allocation in a study. However, a recent survey found that these terms are not interpreted consistently among readers, for example some individuals thought ‘double blinded’ meant that the research subject and the outcomes evaluator are blinded to treatment status, whereas others interpreted ‘double blinded’ as meaning the subject and the treating clinician were blinded to treatment status [ 46 ]. Consequently, the terms ‘single’, ‘double’ or ‘triple’ blinded in themselves have little meaning; a good quality study will fully and completely describe the means of concealing treatment allocation [ 46 ].

Blinding serves two purposes. First, it helps ensure that the nature of the treatments remain distinct and separate between the intervention and control arms of the study [ 38 ]. For example, if the subject knows the study arm to which they belong, there can be a tendency to seek out the treatment being administered in the opposing arm. When this happens, the distinction between the treatment and comparison arm blurs, reducing the internal validity of the study. This phenomena is called ‘cross-over’, which will be discussed shortly.

The second purpose of blinding is to limit prevarication bias and interviewer bias. Prevarication bias occurs when a study subject over- or under-estimates outcome because of knowledge of the kind of treatment they had received. There can be any number of reasons, for example, a subject may subconsciously feel the need to provide answers pleasing to the interviewer or study coordinator, or the subject may feel that there is some secondary gain from consciously mis-reporting their clinical status. Interviewer bias occurs when a clinical outcomes evaluator subconsciously or deliberately influences responses from a subject because of an awareness of which treatment the subject has received. A study can be particularly susceptible to interviewer bias when the same person who provides treatment in a study is also the same person who evaluates outcome at the end of the study. Because clinical personnel have an interest in outcome of a patient, it is likely that person will not be able to fairly evaluate the outcome of the subject. For this reason it is important that outcomes be evaluated by an independent person who has no knowledge of the treatment status of the subject.

Blinding may be very difficult to maintain in studies comparing surgical and non-surgical treatments. For example, it is difficult to conceal a scar from a patient, and some trials, as a policy, reveal the type of implant to the subject [ 8 , 20 ]. However, the effect on study findings from lack of blinding is not trivial. One study found that in studies not using full blinding, estimates of treatment effect were exaggerated by about 19% (32) [ 37 ]. Yet, few studies report the degree to which blinding was maintained during the course of the study [ 31 ].

How can we know whether blinding was maintained? One technique used is to ask the study participant, at the end of the study, which treatment they thought they had received. The response is graded on scale ranging from ‘treatment’, ‘control’ and ‘do not know’. A statistical test compares what the subject guessed they had received against their actual treatment allocation. If blinding was maintained, the test would show a random distribution among those guessing correctly and those guessing incorrectly, that is, about 50% guessing correctly. If blinding was lost, then the proportion guessing correctly would be expected to be different from 50% [ 4 ].

Good outcomes studies will report on the kind of blinding that was employed. At a minimum, an independent outcomes evaluator, who does not know what kind of treatment the patient has received, should be used. The clinician who provides treatment must never also assess outcome. Whenever reasonably possible to do, the patient themselves should be blinded to the treatment that they have received. Finally, a good outcomes study will report in the Discussion section of the paper how blinding (or lack thereof) could have potentially affected the findings of the study.

Control of cross-over

If the subject knows the study arm to which they belong, there can be a tendency to seek out the treatment being administered in the opposing arm. When this happens, the distinction between the treatment and comparison arm blurs, reducing the internal validity of the study. This phenomena is called ‘cross-over’.

Cross-over is a modest methodological issue in studies comparing different kinds of implant technologies, for example when comparing instrumented and non-instrumented fusion. This is because the rate of salvage or revision of spinal implants is low, so the opportunity to exchange one kind of spinal implant with another is limited. However, cross-over is a significant methodological issue when comparing surgical with non-surgical management. A good quality trial will report cross-over, study withdrawals and cases with missing data. Useful information can be gleaned from reasons why subjects withdrew from a study or otherwise were unable to complete the study protocols.

Randomization

Randomization is an allocation procedure that theoretically produces study arms where the overall group characteristics are identical in all respects. This property is guaranteed by the Central Limit Theorem, which states in part that a random selection process will always produce a sample whose average estimates the population mean from which the sample is drawn [ 10 ]. Where several groups are formed by random allocation from a large pool of eligible subjects, the Central Limit Theorem would lead us to expect that the groups would be representative of the total pool of eligible subjects, and by extension, similar to each other. When comparison arms are equivalent in all respects except for the type of treatment received, then the only conclusion that can be drawn is that differences in outcome are attributable solely to the type of treatment given. This logic is the underpinning of why properly randomized controlled trials are thought of as providing the strongest evidence of treatment efficacy [ 10 ].

There are natural barriers to using randomization in spine surgical trials [ 29 ]. One of the most important barriers is the natural disinclination by the surgeon to permit treatment to be left up to chance. Leaving treatment decision up to chance can be thought of as unethical, especially where the surgeon or the patient has a particularly strong belief about the benefit of a particular treatment option. However, recent studies have shown that significant bias can be introduced in randomized controlled trials where the allocation schedule is not concealed from the patients or clinicians [ 46 ].

Consequently, a good randomized controlled trial will report: the method of randomization used, and demonstrate the equivalence of the control and intervention groups by comparing selected, baseline characteristics.

Control of differential drop out during follow-up

It has been shown that measures of functional outcome can be affected by differences in post-surgical management following lumbar fusion [ 42 ]. To avoid biasing comparison between study arms, randomized controlled trials should employ uniform protocols for post-surgical rehabilitation and management [ 14 ]. In addition, problems with patient compliance can confound long-term findings, even when rehabilitation protocols are carefully planned and implemented. Research into patient compliance is evolving, but current thinking is that deviations from prescribed treatment reflect a process whereby a patient tries to accommodate the requirements of treatment into the context of their own, competing, life and situational demands [ 22 ]. The protocols for post-operative management need to be clearly described in any study of surgical intervention. For example does the post-operative protocol of care include certain medication and physical therapy.

Studies can loose stochastic equivalence between study arms when individuals in the study drop-out from follow-up. Drop out can occur for a host of reasons, for example, study participants may die, or move, or they may decide to withdraw their voluntary consent. Cases with missing or incomplete data can be considered a form of drop-out. Study investigators may choose to exclude participants from further follow-up if those individuals require salvage, revision or replacement surgery, or if medical conditions develop that preclude them from continued participation in the study. In the same way that a good trial reports a flowchart of individuals recruiting and enrolling into the study, a good trial will report the number, characteristics and study arm membership of those individuals dropped or lost to follow-up.

Correct statistical analysis

Each study design has its own associated statistical analysis procedure. The case control design expresses associations in terms of the odds ratio; the cohort design uses the relative risk; the clinical trial expresses associations in terms of rate ratios (for categorical outcomes) or differences in mean values (where the outcome in continuously scaled). The reader needs to review the statistical analysis and ensure that, for the findings to be statistically sound, that the statistical analysis methodology matches the study design. Important bias can result where the analysis methodology does not reflect the study design. For example, the use of a unmatched analysis technique in a case control study where matching is used can strongly bias the findings and invalidate the conclusions drawn [ 45 ].

It is beyond the scope of this article to discuss the specific techniques for conducting statistical analyses appropriate for each study design. Numerous texts are available for cross-sectional, [ 26 ] cohort, [ 11 ] case control [ 45 ] and randomized clinical trial study designs, [ 27 , 38 ] and the reader is referred to these texts for more information.

Step 7: determine if the study was adequately powered

There is a need to have sufficient numbers of subjects enrolled in study to avoid drawing erroneous conclusions from the statistical findings. The first error is when the statistical analysis shows that there is a difference in outcome between study arms when in fact differences in treatment efficacy are negligible. This is referred to as a ‘Type I’ statistical error. The second statistical error is referred to as a ‘Type II’ error and occurs when the statistical analysis fails to demonstrate that a difference in treatment efficacy exists when in fact the treatments do produce decidedly different outcomes but the number of subjects were too small. How many subjects need to be enrolled to avoid these two different inferential errors?

A study is sufficiently powered if the differences in outcome the study investigator wishes to demonstrate is larger than the study’s standard error [ 55 ]. The standard error represents the range of possible ‘best guesses’ of the average outcome score among those getting a particular treatment. A ratio, called the effect size, consists of the difference in outcome the investigator wishes to demonstrate (numerator) compared with the study’s standard error (denominator).

The study’s standard error is inversely proportional to the number of subjects enrolled. When the number of subjects enrolled is high, the standard error is low. When the number of study subjects is low, then the standard error is high [ 55 ].

Studies have adequate statistical power when the outcome differences are much larger than the standard error. There are two ways to ensure a study has adequate statistical power: define outcome differences that are very large, or enroll large numbers of study subjects [ 55 ].

Usually a balance is drawn between the two. The study investigator justifies how big a difference in treatment outcome should be tested; a good outcomes study will rationalize this choice by referring to the literature on the minimally clinically significant difference (described earlier). Then the investigator will identify, by means of a sample size calculation, the number of research subjects required to demonstrate this minimally clinically significant difference as statistically significant.

Consequently, to be of best use, clearly indicate the reasons for sizing a study to detect a difference in outcome between treatment arms of a given magnitude and report the results of the sample size calculation that was done in preparation for the study. We believe the best way to determine a correct sample size is to conduct a pilot study on selected outcomes.

Synthesizing information from multiple studies

Conclusions regarding treatment efficacy come from synthesizing available evidence identified from systematic literature searches. There are multiple approaches to synthesizing evidence, one of which can be labeled as a ‘systematic literature review’, and another that can be termed a ‘best evidence synthesis’. The systematic review is characterized by a thorough literature review which grades the methodology of each of the studies included in the review. Usually some sort of grading criteria for each methodological element is developed for the review effort. These grading criteria are agreed upon by the individuals reading and rating the studies before the systematic review takes place. For example, a typical way of rating ‘Blinding’ would be: ‘Single blinded’, ‘Double-blinded’, ‘No blinding’, ‘Not described’ [ 16 ]. Some systematic reviews conduct analyses of inter- and intra-rater consistency in assigning grades, although this practice, while desirable, is still uncommon [ 36 ]. Scores from each of the methodological elements are added together to form and overall quality score for the study. The quality score represents, in essence, how confident the reader can be that important methodological elements that limit bias and confounding are addressed by the design of the study. Examples from the low back pain literature include (but not limited to) the various reviews from the Cochrane Back Pain group (such as the review of surgery for degenerative lumbar spondylosis, [ 31 ] multidisciplinary biopsychosocial rehabilitation for chronic back pain, [ 33 ] and surgery for lumbar disc prolapse [ 31 ] to name few), and evidence reviews of common management techniques for low back pain [ 54 ].

The best-evidence synthesis, on the other hand, can be thought of as a generalization of the systematic literature review. Whereas a systematic literature review grades studies with regard to methodological rigor, the best evidence synthesis combines evidence to summarize what is currently known regarding risk factors, diagnosis, treatment and prognosis of a medical condition. Some examples of best evidence synthesis for spine pain include the Quebec Task Force on Spinal Disorders, [ 48 ] the Quebec Task Force on Whiplash-Associated Disorders, [ 49 ] the Paris Task Force on Back Pain, [ 3 ] and the current ongoing Neck Pain Task Force [ 1 ].

This paper reviews some of the basic methodological considerations when reviewing evidence from outcomes studies, with particular reference to studies of spine surgery. A suggested checklist for drawing attention to key methodological issues is presented. The process of critical evaluation of outcome studies does not necessarily resolve controversy. For example, systematic review, after nine randomized clinical trials, still has not provided definitive evidence whether chronic, non-specific low back pain is best treated with surgical or non-surgical approaches, or which patient best benefit from either approach [ 30 ]. Where one study has important methodological flaws that limit the strength of the evidence, other later studies can address these methodological limitations to test hypotheses. The process of coming to consensus can be slow and difficult. However, for the clinician, the immediate benefit of critical evaluation of the literature is to provide to patients the strongest available evidence for making treatment choices, but also to inform the patient in those situations where weak (or no) evidence exists of efficacy of an existing or new treatment.

Moms’ Moods Linked to Newborn Outcomes

Sarah Maylott, PhD, studies moms-to-be at high risk of mental health challenges and how to prevent those problems from being passed on to their babies. But since she had her first child three and a half months ago, her work hits closer to home.

“I grew up nannying, and I’ve listened to babies crying, and I’ve always felt, ‘I'm good. I’ve got this.’ But now, if a baby cries, I have such a deep reaction to it,” she said. “Even a normal, healthy pregnancy is a struggle sometimes. I can’t imagine how amplified that is for women who are having high emotion dysregulation.”

Emotion dysregulation (trouble with managing emotions, controlling behavior, and achieving goals) is linked to anxiety, depression, substance abuse, and at its most extreme, borderline personality disorder. In a study published earlier this month in the journal Pediatrics Research, Maylott, a postdoctoral associate, and Elisabeth Conradt, PhD , associate professor in the Duke Department of Psychiatry and Behavioral Sciences, documented the complexity of emotional dysregulation in pregnant women and showed that it is linked to neurobehavior in their newborns.

The researchers studied 192 pregnant women at risk for emotion dysregulation, 30 of whom were prescribed medication for opioid use disorder (methadone or buprenorphine). Opioid use during pregnancy is linked to neurobehavioral challenges in newborns, Maylott said, but drug use can be intertwined with emotional dysregulation. “We really wanted to disentangle what was happening here,” she said.

To do that, they measured patterns in the pregnant moms’ “respiratory sinus arrhythmia (RSA) reactivity.” RSA reactivity looks at changes in heart rate that coincide with changes in breathing, Conradt said. “When you inhale, your heart rate increases, and when you exhale, your heart rate decreases. That variability in your heart rate helps you flexibly respond and adapt to changes in the environment.”

RSA reactivity is an accepted marker of emotional regulation, but it has not been well studied in pregnant women, Conradt said. The researchers measured the pregnant women’s RSA reactivity while watching a video of a calming landscape, a video of a child playing, a video of a child crying (more stressful), and then a final calming video. They also measured emotional dysregulation using a self-report questionnaire called the Difficulties in Emotion Regulation Scale.

The researchers expected to find just two RSA patterns in this group of women:  one that was dysregulated and one that was more normal or flexible.

Instead, they found three patterns. Two of them (“increasing” and “blunted”) were linked to higher levels of emotional dysregulation in the moms and more neurobehavioral dysregulation in their newborn babies. Most of the women in the study (65%) who were in treatment for opioid use had a “blunted” profile, Maylott said. "There was a little bit of flexibility in responding over the course of the tasks that they did, but there wasn't much change.”

A third profile (“decreasing”) was more flexible and was linked to low levels of emotional dysregulation in the moms and less neurobehavioral dysregulation in the newborns.

This study included a fairly small sample size, but further study to explore these unexpectedly complex patterns could inform future interventions that could help both baby and mom, Maylott said.

“It’s important to understand this area of rapid development in order to best support parents and infants. Emotion dysregulation is modifiable, and there are interventions that are effective in reducing it,” Maylott said. “These results could have important implications for reducing opioid use and potentially improving newborn neurobehavioral outcomes.”

“A lot of people think that what happens to babies before they're able to form memories doesn't matter,” Conradt said. “But we do know that prenatal exposures and experiences can affect children's outcomes. So it is critical that we support pregnant women and pregnant people as well as their babies.”

Conradt has applied for funding for a clinical study in which she would treat pregnant women with emotion dysregulation at Duke to find out if an intervention can prevent problems for babies before they’re born. One such therapy is dialectical behavior therapy. “It's a really comprehensive therapy that teaches moms how to manage their emotions,” she said.

“Pregnancy is a phenomenal time to intervene with moms who are using opioids because they are so concerned about how their opioid use has affected their baby, and they want to do everything that they can to improve their baby's outcomes,” Conradt said. “I've done a lot of work clinically with moms using opioids. I've never met a mom who isn't concerned about how their use has affected their baby and what they can do to support their baby's outcomes.”

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10 years of convergent neck implants: a systematic review of clinical outcomes, initial original concepts, and changes in surgical and prosthetic protocols.

1. Introduction

2. materials and methods, 2.1. analysis of the scientific evidence concerning prama implant, 2.2. inclusion and exclusion criteria, 2.3. assessment of methodological quality of the included studies, 2.4. grading of recommendations assessment, development, and evaluation (grade), 3. results and discussion, 3.1. state of the art regarding this implant, 3.1.1. outcome analyses (survival rates and hard and soft tissues parameters), 3.1.2. assessment of methodological quality of the included studies, 3.1.3. grading of recommendations assessment, development, and evaluation (grade), 3.2. factors influencing bone tissues around prama implant according to literature, 3.3. factors influencing soft tissues around prama implant according to literature, 3.4. initially proposed protocols (2014–2015), 3.4.1. exposed neck protocol, 3.4.2. submerged neck protocol, 3.4.3. design of prosthetic crowns and soft tissue compression, 3.5. recent clinical innovations and new clinical protocols (2020–2024), digital intraoral scanner workflow, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Zamparini, F.; Spinelli, A.; Gandolfi, M.G.; Prati, C. 10 Years of Convergent Neck Implants: A Systematic Review of Clinical Outcomes, Initial Original Concepts, and Changes in Surgical and Prosthetic Protocols. Appl. Sci. 2024 , 14 , 7568. https://doi.org/10.3390/app14177568

Zamparini F, Spinelli A, Gandolfi MG, Prati C. 10 Years of Convergent Neck Implants: A Systematic Review of Clinical Outcomes, Initial Original Concepts, and Changes in Surgical and Prosthetic Protocols. Applied Sciences . 2024; 14(17):7568. https://doi.org/10.3390/app14177568

Zamparini, Fausto, Andrea Spinelli, Maria Giovanna Gandolfi, and Carlo Prati. 2024. "10 Years of Convergent Neck Implants: A Systematic Review of Clinical Outcomes, Initial Original Concepts, and Changes in Surgical and Prosthetic Protocols" Applied Sciences 14, no. 17: 7568. https://doi.org/10.3390/app14177568

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