Approximately 20% of adults report having chronic pain.21,23,65,86 Unfortunately, response to treatments for chronic pain is often modest and can result in significant side effects including adverse events (AEs).1,11,16,27,34,51,70,81 These realities highlight the need for more effective chronic pain interventions. One challenge in the development of novel treatments is balancing their benefits and risks. An example of this predicament involves the ongoing opioid crisis in the United States, which requires balancing the analgesic benefits of opioid medications with their significant risks, including persisting side effects, dependence potential, and risk of overdose.48,54,73,75,80,87,112 Prescription opioid analgesics provide a timely example of the need to relieve pain while also protecting patients from the risks of pain interventions.
Benefit and risk data are not reported consistently in many randomized clinical trials (RCTs), including chronic pain trials, making it difficult to combine and compare results across studies.8,26,47,50,56,58,61,62,68,82,104,105,115 Moreover, the primary outcomes in clinical trials often focus on treatment benefits (efficacy) rather than on risks, such as AEs.17,69 This is often because studies are designed prospectively to have sufficient power to detect efficacy rather than identify risk.24 In addition, benefits and risks of treatment are most commonly examined as separate outcomes in clinical trials, which cannot address whether there might be a relationship between the two.31 For example, patients who benefit from an intervention could also be the same patients who are more (or less) likely to experience harms (ie, correlated benefit and risk outcomes within the same patients).
Multiple frameworks and methods have been developed to account for benefit and risk outcomes in relation to each other in a combined metric rather than as separate outcomes.8,13,19,20,29,43,50,68,82,83,89,91–93,99,107,113 These methods are diverse and can include qualitative or quantitative steps for combining benefits and risks for each treatment condition (group level assessment).29,37,82 Benefit–risk assessments can also be evaluated at the level of an individual patient and then compared across treatment conditions (individual level assessment).7,31,42,69,71 An additional advantage of benefit–risk assessments is that they can be tailored to best address the demands of a specific trial or other considerations such as patient subgroup differences (eg, age, multimorbidity, type, and intensity of pain). However, the applicability of these benefit–risk composite measures across chronic pain clinical trials has not been adequately evaluated.
This article provides an overview of the steps associated with benefit–risk assessments applied to pharmacological and nonpharmacological RCTs across a range of chronic pain conditions. Our aims are based on an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) consensus meeting and are informed by a review of the benefit–risk assessment tools that have been used in published chronic pain trials or highlighted by key stakeholders (ie, U.S. Food and Drug Administration [FDA], European Medicines Agency, Cochrane, and Outcome Measures in Rheumatology [OMERACT]). Using this information combined with the collective expert opinion of the meeting participants, this article provides considerations for benefit–risk assessment and reporting in RCTs of chronic pain.
Recommendations presented in this article were informed by a 2011 IMMPACT meeting organized by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks public–private partnership with the FDA. The meeting addressed approaches for the assessment and interpretation of benefit–risk in chronic pain clinical trials and other related topics103 (http://www.immpact.org/meetings/Immpact14/participants14.html). In addition, a review of published clinical trials of chronic pain treatments (pharmacological or nonpharmacological) was completed. A summary of the literature review findings is found in the Supplementary Information (available at https://links.lww.com/PAIN/B488). Finally, an internet search of publicly available documents was completed to identify publications and guidance related to benefit–risk assessments specific to chronic pain treatments. Professional organizations that were searched included the FDA, European Medicines Agency (EMA), National Academies of Science, Engineering, and Medicine (NASEM), Cochrane, and Outcome Measures in Rheumatology (OMERACT; an independent initiative of international stakeholders interested in outcome measurement). The documents included for review comprised reports, publications, and white papers. Presentations, web site content, or other informal methods of communication were excluded. Iterative revisions to preliminary drafts of this article were made until co-author consensus on its content was achieved.
2.1. Recommendations for benefit–risk assessment from regulatory agencies and professional organizations
The Cochrane Handbook addresses the importance of reporting the desirable and undesirable health outcomes of clinical trials (listed in order of importance) in the “Summary of findings” tables included in each Cochrane Review.88 In addition, the Handbook provides strategies for assessing benefits and AEs in the same review. For example, owing to differences in coding and categorization of AEs between studies, review authors are instructed to be alert to situations in which the coding of AEs splits data unnecessarily (eg, pain in leg or arm), which may dilute the signal of a more global effect (eg, all patients affected by pain). Likewise, authors are warned that combining AEs into a general outcome (eg, total number of AEs) can only give a broad impression of effects and obscure important differences between the interventions. Finally, Cochrane authors are instructed to include serious AEs (SAEs) in their reporting and note when safety data have not been adequately reported in the literature.
2.1.2. European Medicines Agency
The EMA began a benefit–risk methodology project in 200928,29 (Supplementary Information, available at https://links.lww.com/PAIN/B488). The final report was released in 2012 and recommended the use of the Problem formulation, Objectives, Alternatives, Consequences, Trade-offs, Uncertainties, Risk attitude, and Linked decisions (PrOACT-URL) qualitative framework for evaluating benefit–risk, as well as the inclusion of an “effects table” for conveying benefit–risk information. The EMA also recommended that this qualitative framework be supplemented with a multiple-criteria decision analysis (MCDA) quantitative approach in more complex situations.28,29,68,117 In addition, the EMA provided criteria for evaluating benefit–risk assessment tools and determining their contribution to various types of research,82,89 including (1) logical soundness, (2) comprehensiveness (eg, ability to handle uncertainty), (3) acceptability of results (eg, ability to identify inconsistencies in the data and in people's judgments as well as understandable and interpretable output from the analysis), (4) practicality (eg, analysis is time efficient and can be taught to others easily), and (5) “generativeness” (eg, the benefit–risk approach provides a clear audit trail and the results can be easily understood).
2.1.3. National Academies of Science, Engineering, and Medicine
Eight NASEM reports or workshop summaries that addressed benefit–risk were located (Supplementary Information, available at https://links.lww.com/PAIN/B488). In 2014, the FDA and the Institute of Medicine (now NASEM) convened 2 public workshops on Characterizing and Communicating Uncertainty in the Assessment of Benefits and Risks of Pharmaceutical Products.60 The workshops were designed to address uncertainty in pharmaceutical regulatory decision-making related to variability in human biology, drug chemistry, and clinical trial research. A focus of the summary included existing tools and approaches for communicating scientific uncertainties to a range of stakeholders invested in the results of pharmaceutical benefit–risk assessments (eg, FDA; researchers in academia, government, and regulated industry; policymakers; patient groups; and the public).
2.1.4. Outcome Measures in Rheumatology
Outcome Measures in Rheumatology is an international initiative aimed at improving outcome measurement across rheumatologic conditions, including efforts to simplify the simultaneous assessment of benefits and harms at the individual patient level (Table 1).2,6,7,100 The OMERACT method, referred to as a 3 × 3 methodology, analyzes the benefits and harms simultaneously at the individual patient level (rather than at the group treatment level). This approach can account for the possibility that patients benefiting from the intervention could also be the same patients who are more (or less) likely to experience harms (ie, correlated benefit and risk outcomes within the same patients). The OMERACT method relies on a contingency table that allows for 2 or 3 levels of benefit across 2 or 3 levels of harm. The specific benefit and harm levels are uniquely defined depending on the chronic pain condition(s) and treatment(s) being evaluated and therefore can vary. However, the interpretation of the contingency table is consistent across studies, with an “unqualified success” corresponding to a patient with a good response in the benefit category without any AEs in the harm category. An “unmitigated failure” would involve a patient with no response in the benefit category but at least 1 AE in the harm category. As represented in Figure 1, the OMERACT method was recently applied to data collected from 2 separate rheumatoid arthritis clinical trials (the Treatment of Early Aggressive Rheumatoid Arthritis, or TEAR trial and the Rheumatoid Arthritis Comparison of Active Therapies, or RACAT trial).7 The primary findings from the trials revealed no significant safety concerns of any treatment and significant beneficial effects of treatment relative to comparators in the TEAR trial, but not in the RACAT trial. However, the secondary analysis of benefit–risk in these trials revealed a more complicated pattern of results not identified in the primary analyses. In the secondary analysis, benefit was defined as good, moderate, or no response depending on the patient's disease activity and harms were categorized into 3 types of AE outcomes (no AEs, non-SAEs, and SAEs). The results of the TEAR trial analysis revealed that treatment response and AE rates were weakly associated with no significant difference between the treatment arms). In the RACAT trial, treatment response and AEs were negatively associated such that the frequency of AEs and SAEs increased as beneficial responses decreased. These findings demonstrate that a combined benefit–risk assessment at the individual level can reveal differences in clinical response that are not obvious when benefit and risk are assessed separately. This method is limited because it classifies benefits and AEs categorically, which could oversimplify these outcomes and the final results of the analysis. For example, the AE category that does not include SAEs is very broad and could include a wide range of potential outcomes. Because of these and other limitations, the OMERACT benefit–risk analysis should be considered a complementary method and should not fully replace current analysis and reporting strategies in clinical trials of chronic pain treatments.
Table 1 -
Selected level benefit–risk assessment
frameworks and methods for chronic pain clinical trials.
Group Level Assessments
| EMA PrOACT-URL
||The EMA PrOACT-URL is an 8-step qualitative analysis that provides a generic problem structure for identifying favorable and unfavorable effects, as well as the uncertainty of each, that has been adopted by the EMA.28,29 The framework is based on the field of decision analysis and was developed through the public–private partnership, IMI PROTECT.
| FDA BRF
||The FDA BRF 5-step qualitative framework provides a simple and user-friendly snapshot of benefit–risk assessment that is intended to be broadly applicable.37–39,68 It should be updated as new information that is received and can be used throughout the regulatory process. The 5 steps and questions asked include (1) analysis of the condition/"what is the problem?”; (2) unmet medical need/"what other potential interventions exist?”; (3) benefit; (4) risk/"what am I worried about?”; (5) risk management/"what can I do to mitigate/monitor those concerns?"
||Chronic pain (general)85
| PhRMA BRAT
||PhRMA BRAT is a 6-step qualitative analysis developed to facilitate benefit–risk assessment by pharmaceutical companies and regulators. The method results in a summary table using the following: decision context, outcomes, data sources, framework, outcome importance, and display and interpret key metrics.18,77 Benefits and risks are not integrated in this framework but are assessed separately to reduce complexity.
||Multiple-criteria decision analysis (MCDA) is a quantitative analysis method based on decision theory that combines evaluations of multiple potential benefits and risks (based on prespecified criteria) into a weighted benefit–risk assessment.22,82 The scoring and weighting process allows the effects of different interventions to be placed on a common scale that allows for comparisons across interventions.
||Chronic cancer pain101
||Incremental net health benefit (INHB) is a quantitative analysis method that is based on health-outcomes modeling that incorporates a life-expectancy measure adjusted for quality of life (ie, quality-adjusted life year; QALY).20,44,45 The QALY represents an adjustment to length of life for the quality of life experienced and can be easily adapted to benefit–risk analysis by separating outcomes into expected health improvements with positive QALYs (benefits) and adverse health impacts with negative QALYs (risks). Benefit–risk differentials can then be expressed as either ratios or differences although the latter is preferred because the difference can be interpreted as healthy days (or months or years) of life gained (lost) because the units of measurement are the same. Although standalone use of the INHB in pain populations is rare, clinical benefit–risk (net QALY impact) is the denominator in a range of cost-utility studies that have evaluated pain interventions.
Individual Level Assessments
||The DOOR method is a quantitative analysis that provides a probability of a participant in the active group having a more desirable outcome than a participant in the control group. These probabilities are determined by ranking trial participants based on the desirability of their total experience of benefits and risks, and then the resulting rankings are compared between intervention arms.31,32 A key benefit of DOOR is that its calculation and interpretation are straightforward relative to other benefit–risk assessment methods.
||Not yet examined in a chronic pain population
| ETC measure
||The ETC measure is a quantitative analysis that integrates responder criteria for pain reduction (>20%, >30%, or >50% reduction in pain intensity from baseline) and AEs (no AEs, no or mild AEs, and no or mild drug-related AEs).69 The approach assigns a score for both efficacy and tolerability for each day the patient is in the study, thus accounting for incidence, severity, and duration of AEs in 1 metric. The combination of scores across efficacy and tolerability over time forms a continuous ETC score that generally provides greater statistical power than dichotomous outcomes. The ETC score ranges from 0 to 1 with a clinically intuitive interpretation. For example, a score of 0.45 means the patient's response was “good” with respect to both efficacy and tolerability 45% of the time.
||Chronic low back pain69
||OARSI has provided patient-focused, evidence-based, expert consensus guidelines for the management of knee OA that include the recommendation to perform a quantitative analysis using a composite benefit and risk score.79 The score is voted on across a panel of expert physicians and calculated as the product of the benefit score (on a scale of 1-10) and the transposed risk score (where 1 = highest and 10 = safety) yielding a range of 1 (worst) to 100 (best). The group's mean risk and benefit scores [along with 95% confidence intervals (CIs)] for each treatment are then plotted separately as bar graphs.
||The OMERACT is a quantitative method that relies on a contingency table that allows for 2 or 3 levels of benefit across 2 or 3 levels of harm (Fig. 1).7 The specific benefit and harm levels are uniquely defined depending on the chronic pain condition(s) and treatment(s). The interpretation of contingency table is consistent across studies, with an “unqualified success” being a patient with a good response without any AEs and an “unmitigated failure” being a patient having no benefit but experiencing at least 1 AE.
Additional benefit–risk approaches that might be considered and not highlighted in the present table for brevity, include MCDA, discrete-event simulation, probabilistic simulation, and Bayesian belief networks.109,110
AE, adverse event; BRF, benefit–risk framework; DOOR, Desirability of Outcome Ranking evaluation; EMA, European Medicines Agency; ETC, efficacy–tolerability composite; FDA, United States Food and Drug Administration; IMI PROTECT, Innovative Medicines Initiative Pharmacoepidemiological Research on Outcomes of Therapeutics; INHB, incremental net health benefit; MCDA, multiple-criterion decision analysis; OMERACT, Outcome Measures in Rheumatology; OARSI, Osteoarthritis Research Society International; PhRMA BRAT, Pharmaceutical Research and Manufacturers of America Benefit–Risk Action Team; PrOACT-URL, Problem formulation, Objectives, Alternatives, Consequences, Trade-offs, Uncertainties, Risk attitude, and Linked decisions.
2.1.5. U.S. Food and Drug Administration
The FDA has released a series of documents focused on benefit–risk assessment, including 10 guidance documents (Supplementary Information, available at https://links.lww.com/PAIN/B488). Five of these documents pertain to medical devices, 4 address pharmacological treatments, and 1 spans multiple FDA centers and addresses benefit–risk reporting on the Internet and social media. The FDA currently recommends a structured qualitative benefit–risk framework (BRF) supplemented with quantitative analyses to analyze the benefits and risks associated with medical products.37–39,68 The FDA framework addresses 4 dimensions: (1) analysis of the condition, (2) current treatment options, (3) benefits, and (4) risk management. The FDA has conducted several public meetings on the topic of benefit–risk assessment in recent years, and draft guidance was scheduled to be published in 2020; however, no updates were located to prepare this article.38,40,74 This guidance is expected to use a case study approach for articulating FDA's decision-making context for benefit–risk analysis to provide stakeholders with a clearer understanding of how considerations of a medication's benefits vs risks factor into FDA's regulatory decisions throughout the drug development lifecycle, including premarket and postmarket phases. Importantly, this guidance will discuss how relevant patient experience data and related information may be used to inform benefit–risk assessment.
3. Recommendations for benefit–risk assessment and reporting in chronic pain clinical trials
Terminology associated with benefit–risk assessment, including operational definitions of key terms, is not standardized and often vary.28,60 Opinions vary as to whether the terms “harm” or “tolerability” might be more appropriate than the term “risk”.60,69 For this article, we define benefits as the intended favorable effects for the target population associated with an intervention and risks as the unintended clinical and health outcomes or detrimental effects that can be attributed to the intervention.25 The term risk in the present review includes unwanted side effects, some of which will have an adverse effect on patient functioning, but also includes major safety risks such as myocardial infarction or death. We recommend researchers distinguish between risks attributed to the treatment under study (eg, chronic nausea or vomiting) relative to those that are most likely not related to the treatment per se (eg, an injury sustained during a motor vehicle accident). We define benefit–risk assessment as a structured method (qualitative or quantitative) for combining separate benefit and risk outcomes into a composite metric that allows for a clear comparison of benefits and risks in relation to each other at the level of the group or for individual patients. According to our definition, global ratings of patient functioning (eg, Patient Global Impression of Change) that do not specifically include harms would not be considered benefit–risk assessment tools. The ratio of the number needed to treat and number needed to harm could be considered a measure of benefit–risk. We do not consider this approach further because the widely varying definitions used for the number needed to harm preclude meaningful treatment comparisons.103
3.2. Steps associated with benefit–risk assessment
There are 5 steps underlying decision-making related to benefits and risks that are common across a range of disciplines (Table 2).50,57,76,82,92,113
Table 2 -
Steps to consider in benefit and risk assessments in clinical trials of chronic pain treatments.
| Specify the chronic pain condition(s) under study and the currently available treatments for the condition(s). Unmet clinical needs associated with the condition and contextual information such as common comorbidities associated with the condition should also be addressed.
| Identify the key outcomes that will be used to assess the benefits (eg, reductions in pain intensity or severity) and risks (AEs and reduced quality of life). Patient preference on meaningful benefit and risk outcomes should be incorporated at this level, and patient-reported outcomes should be used to gather data.
| Collect and combine data related to the benefits and risks of an intervention(s) in a way that allows for the ranking or weighting of data. In general, 2 approaches to benefit–risk analyses can be performed: compare and combine at the level of the intervention or combine and compare at the level of the individual patient.
| The interpretation of data should incorporate value judgments or trade-offs between the relative importance of benefits and risks in a particular situation, which can vary depending on the type of stakeholder (patient, clinician, and regulatory agency). This step should also address the uncertainty associated with the analysis given that benefit–risk assessments are dynamic and evolve as information changes over time.
| Communicate the results of the analysis, including sharing the processes and rationale leading to the final conclusions. Messaging of the findings might need to be tailored depending on the audience and information should be summarized in succinct, transparent, and user-friendly ways (eg, graphical representations).
The first sequential step involves providing a description of the chronic pain condition(s) examined, current treatments for the condition(s), and any other related contextual information specific to the pain condition that could influence relevant risks, including epidemiological information related to patient demographics or comorbid health conditions (eg, tobacco use, obesity, and concurrent medication use). In addition, the collection of patient preference data at the start of the study to determine patient attitudes regarding benefit–risk has been suggested as an important feature of this step.68
3.2.2. Identify (outcomes and assessments)
The second step requires identification of the key outcomes and measures that will be used when combining benefits and risks. As presented in Table 3 and in the Supplementary Information (available at https://links.lww.com/PAIN/B488), benefits and risks can be assessed using a variety of outcome measures with the most common being reductions in pain intensity (benefits) and AEs (risks). More nuanced outcomes including health-related quality of life, sleep, physical and cognitive functioning, mental health, type or severity or duration of AEs, and abuse liability might also be of interest.108 Simply analyzing the frequency of AEs or SAEs or combining different types of AEs into 1 heterogenous outcome can fail to detect important group differences in harms that are revealed when severity and duration of AEs are incorporated into analyses.69,88 As discussed in detail elsewhere,96 it is essential to consider the use of standardized language when referring to benefits and risks to facilitate the comparison and evaluation of study outcomes (eg, Medical Dictionary for Regulatory Activities and the Systematized Nomenclature of Medicine–Clinical Terms terminologies).
Table 3 -
||Description or definition
||The intended positive or favorable effects of an intervention for the target population (often referred to as “benefits” or “clinical benefits”) that are associated with an intervention.25 Examples include reduction in pain intensity, increase in the number of pain-free days, function, and quality of life.
||The unintended negative clinical and health outcomes or detrimental effects that can be attributed to the intervention. The use of the term risk in this article includes side effects, some of which will have an adverse effect on patient functioning, but also includes safety risks, SAEs such as myocardial infarction, or death. The intensity and duration of all treatment-emergent AEs should be collected (total, severe, and serious), as well as the use of active capture, which includes interviews or questionnaires.25,56
||A structured method (qualitative or quantitative) for combining separate benefit and risk outcomes into a composite metric that allows for a clear comparison of benefits and risks in relation to each other at the level of the group or for individual patients.
||The ability of a clinical test result(s) to inform a decision that positively changes the outcome of a patient106
||Qualitative or descriptive frameworks provide stepwise instructions for evaluating and balancing benefit and risk, including their frequency and duration, and fully describes how that information weighs into decision-making.92 Examples include the Problem formulation, Objectives, Alternatives, Consequences, Trade-offs, Uncertainties, Risk attitude, and Linked decisions framework and the United States Food and Drug Administration Benefit-Risk Framework.
||Quantitative frameworks provide explicit methods for combining and weighing risks and benefits. A quantitative approach may help to improve the transparency of a review, relative to a qualitative approach, by being explicit about how benefits and harms are estimated and compared (Boyd et al., 2012). Although quantitative approaches can be used to examine benefit–risk at the level of the group, they are most commonly used for analyses that begin at the level of the individual patient (). Examples include multiple-criteria decision analysis (MCDA), discrete-event simulation, probabilistic simulation, and Bayesian belief networks.82,83
||Patient preferences represent patient's attitudes toward a set of alternatives necessary for decision-making.60 Collecting data related to a patient's perspective or preference should be taken into account at all stages of research including planning of the clinical trial design and the identification of patient-relevant outcomes.8,63,64,111
|Standardization and Transparency
||A systematic and transparent evaluation process that allows for consistency of reporting, replication, and pooling of data across studies.56
AE, adverse event; SAE, serious AE.
We recommend this step incorporate the needs and preferences of patients into study planning in 2 ways. First, as noted, the choice of benefit–risk outcomes should be based, at least in part, on feedback from patients, surrogates, or patient advocacy groups, and not simply chosen based on clinician, investigator, or regulatory considerations.84,97 Although validated measures of patient preferences are currently lacking in the field, we recommend that at least some measures of benefit and risk include patient-reported outcomes or data reported by patients without interpretation by someone else.2,4,5,36 We recommend that such data be collected through active capture using structured interviews or questionnaires, as well as passive capture or general inquiries, which can identify unanticipated outcomes.25 A detailed discussion and framework for incorporating patient preference data in benefit–risk assessment can be found elsewhere.55
Medical conditions and associated symptoms and interventions can also influence patient preferences or perceptions of benefit–risk trade-offs.2,3,15,49,52 One example includes older patients with knee osteoarthritis who are sometimes willing to forgo greater treatment effectiveness for a lower risk of AEs,41 whereas there is a large body of work demonstrating that individuals with a range of complex, chronic health conditions, including Crohn disease, irritable bowel syndrome, low back pain, and osteoarthritis, are willing to accept high levels of risk in return for disease-modifying benefits of treatment.53,66,67,95,98,109 These observations highlight the potential for subgroup differences among chronic pain populations that can influence the weighting of benefits and risks (eg, age, drug use and dependence history, and multimorbidity).86,109 Finally, this step should include prospective registration of the trial characteristics, including study objectives and hypotheses33,102 and benefit–risk assessments that are planned in a public database(s) such as ClinicalTrials.gov.
3.2.3. Evaluate (end points and analyses)
The third step involves collecting data related to the benefits and risks of an intervention(s) and combining those data in a way that allows for the ranking or weighting of data in a combined metric. A variety of benefit–risk assessments apply to clinical trials of chronic pain treatments (Table 1).8,13,29,46,50,82,83,113 Two approaches to benefit–risk data include those that combine benefit and risk data at the group level and those that first combine such data at the individual level and then analyze differences on the group level.30–32 The most common approach involves summarizing benefit–risk data at the level of the group or intervention (placebo vs active treatment) and then combining these data in a way that allows comparisons across treatments. This approach has the advantage that it is easy to analyze outcomes and quickly communicate the findings and examples include the FDA's BRF and the EMA's PrOACT-URL (Section 4.2). However, this approach does not account for associations between benefits and risks that might occur at the level of the individual patient. For example, a patient who is experiencing the greatest reduction in pain from an intervention could also be more likely to experience SAEs from the same intervention.6,7
An alternative approach involves assessing benefit–risk trade-off within each participant.31,47,94 Examples of benefit–risk assessments that focus on the individual rather than group level analysis are represented in Table 1 and include the Desirability of Outcome Ranking, efficacy–tolerability composite (ETC), OMERACT, and OARSI methods. In the Desirability of Outcome Ranking method trial, participants are first ranked based on the desirability of their total experience of benefits and risks (across multiple dimensions/outcomes), with a focus on the outcomes that are most important from the patient's perspective.30–32 The resulting rankings are then compared between intervention arms (Table 4).
Table 4 -
Analysis of Patients by Treatment from Evans and Follmann (2016).
||Treatment A efficacy
||Treatment B efficacy
||Treatment C efficacy
Table is reproduced from a previous publication and copyright permissions were approved by Taylor & Francis.31
The table represents 4 patient outcomes as a function of efficacy and toxicity. In all groups, 50% of the patients experience beneficial effects of treatment (efficacy). The interpretation of these outcomes is different when the risks of treatment (toxicity) are combined in the analysis. In treatment A, efficacy and toxicity were uncorrelated resulting in 40 patients who had efficacy without toxicity. In treatment B, efficacy and toxicity were positively correlated resulting in 0 patients who had efficacy without toxicity. In treatment C, efficacy and toxicity were negatively correlated resulting in 50 patients who had efficacy without toxicity.
A last point to consider is that under ideal circumstances, benefit–risk analyses should be compared across different subpopulations that represent different demographic factors and comorbidities.6,7,31,53 There could be important subgroup differences that can affect the findings from a benefit–risk assessment. For instance, the risks of some pharmacologic treatments can be significantly greater in patients with impaired renal function; thus, the benefit–risk relationship may be quite different in this subgroup of patients relative to the overall study population.
The fourth step incorporates the perspectives of a range of stakeholders (patients, patient advocacy groups, healthcare providers, payers, pharmaceutical and device companies, and regulatory agencies) seeking improved treatments for chronic pain, each of whom have a unique perspective on the benefits and risk trade-offs.13,37,38,68,72,76,110 These various viewpoints add a necessary complexity to benefit–risk assessment.6,64,68,72,92 For this reason, we recommend that the interpretation of benefit–risk analyses be as transparent as possible with a clear history of the evaluation process that represents each step taken, including the various stakeholders involved in interpreting the evidence.10,82,89 An additional consideration is the need to account for uncertainty when interpreting benefit–risk findings, including statistical uncertainty, especially for outcomes with low incidence rates such as SAEs. Such uncertainty can also be augmented by accounting for missing data associated with patients who stop their treatment or withdraw early from trials for reasons such as perceived lack of efficacy and adverse side effects.12 Statistical approaches for addressing intercurrent events and sources of missing data are evolving and are highlighted by the International Council for Harmonisation guidance (E9/R1).14,59
The final step includes communicating and reporting the results of the analysis, including sharing the processes and rationale leading to the final conclusions.76 This step requires that the presentation of the benefit–risk findings can be understood by the target audience (eg, an individual patient, clinicians, researchers, and the public). Basic principles of effective communication apply here, including (1) providing the information needed for effective decision-making which requires an understanding of the patient's perspective, (2) allowing access to information (eg, graphical representations), and (3) ensuring that users can comprehend the information (eg, health literacy).35 Composite outcomes such as benefit–risk assessments can be challenging to interpret given that a significant result associated with a composite outcome might not indicate a significantly more beneficial treatment depending how the composite was created.46 Thus, information should be summarized in succinct, transparent, and user-friendly ways, including graphical representations to the extent possible rather than data heavy text or tables.30,116
3.3. Selected benefit–risk assessment frameworks and methods
Table 1 describes 9 benefit–risk assessment frameworks and methods that are well-suited for clinical trials of chronic pain treatments. The frameworks and methods identified in the table can be complementary and used simultaneously and include tools that combine benefit–risk at the group level (EMA PrOACT-URL, FDA BRF, incremental net health benefit/INHB, and Pharmaceutical Research and Manufacturers of America Benefit–Risk Action Team/PhRMA BRAT), as well as methods that combine benefit–risk at the level of the patient (DOOR, efficacy-tolerability composite/ETC, measure, Osteoarthritis Research Society International/OARSI Knee Osteoarthritis Model, and the OMERACT method). Few studies have evaluated the various benefit–risk methods described here in clinical trials of chronic pain treatments. For some of these methods, it is possible to use existing clinical trial data sets to evaluate benefits and risks in a combined metric.7,69
We recommend that benefit–risk assessments be used in chronic pain RCTs to combine benefits and risks at the treatment group level (eg, FDA BRF or PhRMA BRAT)18,37,38,77 and at the level of the individual patient (eg, OMERACT or DOOR)2,32 (Table 1). The recommendation to include both types of evaluations is based on the observation that individual differences in clinical response can be obscured when combined at the group level. In many circumstances, it is valuable to include both levels of analysis (group and individual levels). It should be emphasized that there is not a “one-size-fits-all” benefit–risk assessment tool for all chronic pain RCTs and that a combination of methods, as represented in Table 1, may be needed depending on the unique circumstances associated with the treatment, chronic pain condition, and clinical trial. Relatedly, given the diversity of benefit–risk assessment tools that can be used across clinical trials, researchers should be as transparent as possible when reporting how benefits and risks have been defined, measured, and combined to facilitate the application of study findings to patient care and decision-making.
These recommendations can serve as a starting point for incorporating benefit–risk assessment tools into future chronic pain clinical trials. One important component of a research agenda is evaluating and comparing the properties (eg, reliability, validity, and assay sensitivity) of currently available BRFs and methods to determine whether there are approaches that are more informative.7,69 There is a need to integrate, to the greatest extent possible, benefit–risk assessment in clinical trials with other types of relevant data such as those derived from preclinical and epidemiological studies.9,90 This approach could include using health outcomes modeling as a framework, postapproval, epidemiological data regarding the benefits and harms of a particular chronic pain treatment could be combined with individual level data to update earlier benefit–risk assessments,and further guide patient and clinician shared decision-making as well as continued drug development and safety monitoring.44 The systematic assessment of benefit–risk in clinical trials can enhance the clinical meaningfulness of RCT results. We are optimistic that BRFs and methods will be more widely incorporated in future clinical trials of chronic pain treatments.
Conflict of interest statement
The views expressed in this article are those of the authors, some of whom were, or currently are, employees of pharmaceutical, consulting, or contract research companies and may have financial conflicts of interest related to the issues discussed in this article. At the time of the meeting on which this article is based, several authors were employed by pharmaceutical companies and others had received consulting fees or honoraria from 1 or more pharmaceutical or device companies. Authors of this article who attended the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) meeting and were not employed by industry or government at the time of the meeting received travel stipends, hotel accommodations, and meals during the meeting provided by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public–private partnership with the U.S. Food and Drug Administration (FDA). ACTTION has received research contracts, grants, or other revenue from the FDA, multiple pharmaceutical and device companies, philanthropy, and other sources. Preparation of background literature reviews and this article was supported by ACTTION. No funding from any other source was received for the meeting nor for the literature reviews and article preparation. No official endorsement by the FDA, U.S. National Institutes of Health, or the pharmaceutical and device companies that have provided unrestricted grants to support the activities of ACTTION should be inferred. B.A. Kleykamp has received in the past 36 months support from the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks public–private partnership with the United States Food and Drug Administration and compensation for medical writing from Palladian Associates, Hayes Inc/TractManager, STATinMED, and PBS Next Avenue. In addition, between 2014 and 2018 during her previous employment at the consulting firm, PinneyAssociates, she provided consulting advice to pharmaceutical companies, the e-cigarette company NJOY, and the tobacco company, RAI Services Company on noncombustible tobacco products including e-cigarettes. R.H. Dworkin has received in the past 5 years research grants and contracts from the U.S. Food and Drug Administration and the U.S. National Institutes of Health and compensation for serving on advisory boards or consulting on clinical trial methods from Abide, Acadia, Adynxx, Analgesic Solutions, Aptinyx, Aquinox, Asahi Kasei, Astellas, AstraZeneca, Biogen, Biohaven, Boston Scientific, Braeburn, Cardialen, Celgene, Centrexion, Chromocell, Clexio, Collegium, Concert, Confo, Decibel, Dong-A, Editas, Eli Lilly, Ethismos (equity), Eupraxia, Glenmark, Gloriana, Grace, Hope, Immune, Lotus, Mainstay, Merck, Neumentum, Neurana, NeuroBo, Novaremed, Novartis, Olatec, Pfizer, Phosphagenics, Quark, Reckitt Benckiser, Regenacy (also equity), Relmada, Sanifit, Scilex, Semnur, SIMR Bio, SK Life Sciences, Sollis, SPRIM, Teva, Theranexus, Trevena, Vertex, and Vizuri. D.C. Turk has received in the past 36 months support from research grants and contracts from U.S. Food and Drug Administration, U.S. National Institutes of Health, and the Patient-Centered Outcomes Research Institute, and compensation for consulting on research methods and reporting from AccelRx, Eli Lilly, Flexion, GlaxoSmithKline, Novartis, and Pfizer. Z. Bhagwagar employee at, and own stock in, Bristol Myers Squibb and Alexion Pharmaceuticals. S.S. Ellenberg serves on data monitoring committees for Bristol Myers Squibb, Novartis, Marinus, and Rigel Pharmaceuticals. S.R. Evans receives grant support from NIAID/NIH and reports personal fees from Takeda, Pfizer, Roche, Novartis, ACTTION, Genentech, Amgen, American Statistical Association, FDA, Osaka University, National Cerebral and Cardiovascular Center of Japan, NIH, Society for Clinical Trials, DeGruyter, AstraZeneca, Teva, Austrian Breast & Colorectal Cancer Study Group (ABCSG)/Breast International Group (BIG) and the Alliance Foundation Trials (AFTs), Taylor and Francis, Vir, Shire, Alexion, Gilead, Clinical Trials Transformation Initiative, Tracon, Deming Conference, Antimicrobial Resistance and Stewardship Conference, Advantagene, Cardinal Health, Microbiotix, Stryker, Atricure, BENEFIT, Roivant, Neovasc, Nobel Pharma, Horizon, Roche, Rakuten, Duke University, U. of PENN, Takeda, Nuvelution, Abbvie, Clover, FHI Clinical, Lung Biotech, SAB Biopharm, CIOMS, SVB LEERINK. J.S. Gewandter, in the past 36 months, has received research grants from the NIH and consulting income from Disarm Therapeutics, AlgoTX, Asahi Kasei Pharma, Magnolia Neurosciences, Mitobridge, Orthogonal Neurosciences, Science Branding Communications, and SK Life Science. L.P.Garrison received research grants, consulting fees, and/or speaker fees in the past 3 years from Pfizer, Eli Lilly, Biogen, Merck, Roche, Genentech, Novartis, AveXis, BMS, Medtronic, Mallinckrodt, Amgen, AbbVie, Adamas, Analysis Group, Thrive Detection, Medtronic, Roche Molecular Systems, BioMarin, Global Blood Therapeutics, Sanofi-Pasteur, Medtronic, Aspen Institute, and Premera. V. Goli has been an employee of IQVIA in the past 3 years and is currently an employee of Syneos Health. J.T. Farrar reports grants from NIH-NIDDK - U01 Grant (CoI), grants from NIH-NINDS - U24 Grant (PI), grants from FDA-BAA Contract, during the conduct of the study, and personal fees from Pfizer, Daiichi Sankyo, Cara Therapeutic, Biogen, Opioid Postmarketing Consortium, NIH-NIA, Analgesic Solutions, Novartis, Aptinyx, DepoMed, Jansen, Evadera, Eli Lilly, Vertex, outside the submitted work. R.L. Freeman has received personal compensation and/or stock options for serving on scientific advisory boards of AlgoRx, Allergan, Applied Therapeutics, Clexio, Cutaneous NeuroDiagnostics, Glenmark, GlaxoSmith Kline, Eli Lilly and Company, Lundbeck, Novartis, NeuroBo, Regenacy, and Vertex. Received personal compensation for his editorial activities (editor) with Autonomic Neuroscience: Basic and Clinical. Received research support from the National Institutes of Health (U54NS065736, 1R01NS10584401A1, and R01HL111465-01A1). S. Iyengar is an employee of NINDS/NIH, Adjunct Senior Research Professor, IU School of Medicine, Indianapolis, IN 46202. M.P. Jensen has received in the past 36 months research grants from the U.S. National Institutes of Health, the U.S. Department of Education, the Administration of Community Living, the Patient-Centered Outcomes Institute, and National Multiple Sclerosis Society, the International Association for the Study of Pain, Zogenix, Inc, and the Washington State Spinal Injury Consortium. N.P. Katz is an employee of WCG Analgesic Solutions, a consulting company with multiple clients in the pharmaceutical and medical device industries. Dr. Katz does not receive any direct payments from clients. J.P. Kesslak was a salaried employee of Allergan with stock options at the time of the IMMPACT meeting. E.A. Kopecky was a salaried employee with stock options of Endo Pharmaceuticals, Inc at the time of the IMMPACT meeting. D. Lissin was an employee of DURECT Corporation at the time of the IMMPACT meeting and is currently the Chief Medical Officer of Scilex Pharmaceuticals, Inc. J.D. Markman reports nonfinancial support from Pfizer Inc and Eli Lilly, during the conduct of the study; grants and other from Clexio, grants from Pfizer, other from Teva, other from Quark, other from Biogen (Convergence), other from Nektar, other from ENDO, other from Immune Pharma, other from Chromocell, other from Collegium, other from Purdue, other from Novartis, grants and other from Depomed, other from Allergan, other from Sanofi, other from Aptinyx, other from Daiichi Sanyko, other from Plasma surgical, other from Grunenthal, other from Clexio Bioscience, other from Editas Medicine, other from Trevena, other from Inspirion, other from Merck, other from Esteve Pharmaceuticals, other from Tremeau Pharmaceuticals, other from Sophren Pharmaceuticals, and other from YellowBlack Corporation, outside the submitted work. M.P. McDermott has been supported in the past 36 months by research grants from NIH, FDA, SMA Foundation, Cure SMA, Friedreich's Ataxia Research Alliance, Muscular Dystrophy Association, ALS Association, and PTC Therapeutics, has received compensation for consulting from Fulcrum Therapeutics, Inc, and NeuroDerm, Ltd., and has served on Data and Safety Monitoring Boards for NIH, AstraZeneca, Eli Lilly and Company, Catabasis Pharmaceuticals, Inc, Vaccinex, Inc, Cynapsus Therapeutics, Voyager Therapeutics, Neurocrine Biosciences, Inc, and Prilenia Therapeutics Development, Ltd. P.J. Mease has received research grants, consulting fees, and/or speaker fees from Abbvie, Amgen, Bristol Myers, Boehringer Ingelheim, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Novartis, Pfizer, SUN Pharma, and UCB. K.V. Patel has received in the past 36 months research grants from the U.S. National Institutes of Health and Centers for Disease Control and Prevention. S.N. Raja has received in the past 36 months support from research grants, U.S. National Institutes of Health, and is a co-investigator in a research grant from Medtronic, Inc. He is a consultant for Allergan, Averitas Pharma, Bayer, and Lexicon Pharmaceuticals and has consulted in the past for Aptinyx Inc, and Heron Therapeutics. M.C. Rowbotham has provided consulting services to CODA Biotherapeutics, Sustained Therapeutics, SiteOne Therapeutics, Acadia Pharmaceuticals, Editas Medicine, and Amygdala Neurosciences. He serves on a DSMB for Helixmith and is the Treasurer of the International Association for the Study of Pain. C. Sampaio receives salary from CHDI management/CHDI Foundation Inc, and consultancy fees from vTv Therapeutics, Pinteon Pharmaceuticals, Pfizer, Kyowa Kirin, and Neuraly. J.A. Singh has received consultant fees from Crealta/Horizon, Medisys, Fidia, PK Med, Two labs Inc, Adept Field Solutions, Clinical Care options, Clearview healthcare partners, Putnam associates, Focus forward, Navigant consulting, Spherix, MedIQ, Jupiter Life Science, UBM LLC, Trio Health, Medscape, WebMD, and Practice Point communications, and the National Institutes of Health and the American College of Rheumatology. J.A. Singh owns stock options in TPT Global Tech, Vaxart pharmaceuticals, and Charlotte's Web Holdings, Inc. J.A. Singh previously owned stock options in Amarin, Viking, and Moderna pharmaceuticals. JAS is on the speaker's bureau of Simply Speaking. JAS is a member of the executive of Outcomes Measures in Rheumatology (OMERACT), an organization that develops outcome measures in rheumatology and receives arms-length funding from 8 companies. J.A. Singh serves on the FDA Arthritis Advisory Committee. JAS is the chair of the Veterans Affairs Rheumatology Field Advisory Committee. J.A. Singh is the editor and the Director of the University of Alabama at Birmingham (UAB) Cochrane Musculoskeletal Group Satellite Center on Network Meta-analysis. J.A. Singh previously served as a member of the following committees: the American College of Rheumatology's (ACR) Annual Meeting Planning Committee (AMPC) and Quality of Care Committees, the Chair of the ACR Meet-the-Professor, Workshop and Study Group Subcommittee, and the co-chair of the ACR Criteria and Response Criteria subcommittee. I. Steigerwald is an employee of Neumentum, Inc (Chief Medical Officer, SVP) and was an employee of Grünenthal GmbH (Head of Medical Affairs New Brands, SBU Europe, Australia, and North America, VP) at the time of the 2011 IMMPACT meeting. V. Strand is a member of the executive of Outcomes Measures in Rheumatology (OMERACT), an organization that develops outcome measures in rheumatology and receives arms-length funding from 12 companies. L.A. Tive is a salaried employee with stock options of Pfizer Pharmaceuticals Inc. J. Tobias was an employee of NeurogesX, Inc at the time of the meeting and is currently Partner and Managing Director of Aquila Consulting Group, LLC. A.D. Wasan is a consultant for Vertex Pharmaceuticals. H.D. Wilson was a salaried employee of the University of Washington at the time of the meeting, is currently a salaried employee of the pharmaceutical company Boehringer Ingelheim, and within the past 36 months was previously a salaried employee of a scientific consulting firm Evidera.The remaining authors have no conflicts interest to declare.
Appendix A. Supplemental digital content
Supplemental digital content associated with this article can be found online at https://links.lww.com/PAIN/B488.
The authors thank Valorie Thompson and Andrea Speckin for their assistance in organizing the meeting on which this article is based, and Linda Hasman, at the University of Rochester School of Medicine and Dentistry, for her assistance in building the search strategy. The authors acknowledge Laurie Burke for the feedback she provided on an earlier draft of this manuscript and Stephen Morley for his participation in the IMMPACT meeting, and who passed away before publication.
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