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Methods Guide – Chapter – Oct. 5, 2009

Assessing Harms when Comparing Medical Interventions

Formats

This is a chapter from the methods guide, "Methods Guide for Effectiveness and Comparative Effectiveness Reviews."

This paper has also been published in edited form: Chou R, Aronson N, Atkins D, et al. AHRQ Series Paper 4: Assessing harms when comparing medical interventions: AHRQ and the Effective Health Care Program. J Clin Epidemiol 2010; 63, 502-512.

Table of Contents

Comparative Effectiveness Reviews are systematic reviews of existing research on the effectiveness, comparative effectiveness, and harms of different health care interventions. They provide syntheses of relevant evidence to inform real-world health care decisions for patients, providers, and policymakers. Strong methodologic approaches to systematic review improve the transparency, consistency, and scientific rigor of these reports. Through a collaborative effort of the Effective Health Care (EHC) Program, the Agency for Healthcare Research and Quality (AHRQ), the EHC Program Scientific Resource Center, and the AHRQ Evidence-based Practice Centers have developed a Methods Guide for Comparative Effectiveness Reviews. This Guide presents issues key to the development of Comparative Effectiveness Reviews and describes recommended approaches for addressing difficult, frequently encountered methodological issues.

The Methods Guide for Comparative Effectiveness Reviews is a living document, and will be updated as further empiric evidence develops and our understanding of better methods improves. Comments and suggestions on the Methods Guide for Comparative Effectiveness Reviews and the Effective Health Care Program can be made at www.effectivehealthcare.ahrq.gov.

This document was written with support from the Effective Health Care Program at AHRQ. Dr. Moher is supported by a University of Ottawa Research Chair. None of the authors has a financial interest in any of the products discussed in this document.

Suggested citation: Chou R, Aronson N, Atkins D, et al. Assessing harms when comparing medical interventions. In: Agency for Healthcare Research and Quality. Methods Guide for Comparative Effectiveness Reviews [posted November 2008]. Rockville, MD. Available at: http://www.effectivehealthcare.ahrq.gov/index.cfm/search-for-guides-reviews-and-reports/?pageaction=displayproduct&productid=318.

This paper has also been published in edited form: Chou R, Aronson N, Atkins D, et al. Assessing harms when comparing medical interventions: AHRQ and the Effective Health-Care Program. J Clin Epidemiol 2008 Sep 25 [Epub ahead of print].

Authors

Roger Chou, M.D.a

Naomi Aronson, Ph.D.b

David Atkins, M.D., M.P.H.c

Afisi S. Ismailad

Pasqualina Santaguida, Ph.D.d

David H. Smith, Ph.D.a,e

Evelyn Whitlock, M.D., M.P.H.a,e

Timothy J. Wilt, M.D., M.P.H.f

David Moher, Ph.D.g

a Oregon Evidence-based Practice Center, Oregon Health & Science University, Portland, OR.

b Blue Cross Blue Shield Evidence-based Practice Center, Blue Cross Blue Shield Association, Chicago, IL.

c Department of Veterans Affairs, Washington, DC.

d McMaster Evidence-based Practice Center, McMaster University, Hamilton, ON.

e Oregon Evidence-based Practice Center, Kaiser Center for Health Research, Portland, OR.

f Minnesota Evidence-based Practice Center, Minneapolis VA Center for Chronic Disease Outcomes Research, MN.

g University of Ottawa Evidence-based Practice Center, University of Ottawa, Ottawa, ON.

The findings and conclusions in this document are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views of AHRQ, the Veterans Health Administration, or the Health Services Research and Development Service. Therefore, no statement in this report should be construed as an official position of these entities, the U.S. Department of Health and Human Services, or the U.S. Department of Veterans Affairs.

Key Points

  • Assess all important harms, whenever possible.
  • Use multiple sources of information, including clinical experts and stakeholders, to identify important harms.
  • Use consistent and precise terminology when reporting data on harms, and avoid terms implying causality unless causality is reasonably certain.
  • Gather evidence on harms from a broad range of sources, including observational studies, particularly when clinical trials are lacking; when generalizability is uncertain; or when investigating rare, long-term, or unexpected harms.
  • Do not assume studies adequately assess harms because methods used to assess and report benefits are appropriate; rather, evaluate how well studies identify and analyze harms.
  • Be cautious about drawing conclusions on harms when events are rare and estimates of risk are imprecise.
  • Include placebo-controlled trials, particularly for assessing uncommon or rare harms, but be cautious about relying on indirect comparisons to judge comparative risks, and evaluate whether studies being considered for indirect comparisons meet assumptions for consistency of treatment effects.
  • Avoid inappropriate combining of data on harms, and thoroughly investigate inconsistent results.

Introduction

Comparative Effectiveness Reviews (CERs) are systematic reviews that evaluate evidence on alternative interventions in order to help clinicians, policymakers, and patients make informed treatment choices.1 To generate balanced results and conclusions, it is important for CERs to address both benefits and harms.2 However, assessing harms can be difficult. Benefits have been accorded greater prominence when reporting trials, with little effort to balance assessments of benefits and harms. In addition, systematically reviewing evidence for all possible harms is often impractical, as interventions may be associated with dozens of potential adverse events. Furthermore, there are often important tradeoffs between increasing comprehensiveness and decreasing quality of harms data.3

Adequately assessing harms requires CER authors to consider a broad range of data sources. For that reason, they need to deal with important challenges, such as choosing which types of evidence to include, identifying studies of harms, assessing their quality, and summarizing and synthesizing data from different types of evidence.

Identifying Harms to Be Evaluated

CERs should always assess harms that are important to decisionmakers and users of the intervention under consideration.4 High-priority harms should include the most serious adverse events; they may also include common adverse events and other specific adverse events important to clinicians and patients. CER authors should examine previously published reviews, review publicly available safety reports from the U.S. Food and Drug Administration (FDA), and consult with technical experts and patients to set priorities for evaluating harms. Searches on postmarketing surveillance databases may also help identify important potential harms. The methods sections of the CER should specify the process used to identify harms of interest and list the specific harms for which evidence was sought.

Terminology

Terminology related to reporting of harms is poorly standardized.5 This can cause confusion or result in misleading conclusions. CER authors should strive for consistent and precise usage of terminology when reporting data on harms. For example, the term "harms" is generally preferred over the term "safety" because the latter sounds more reassuring and may obscure important concerns. "Harms" is also preferable to the term "unintended effects," which could refer to either beneficial or harmful outcomes. Terms that do not imply causality (such as "adverse events") should be the default term to describe harms, unless causality is reasonably certain.

Definitions for commonly used terms for harms reporting are summarized in Table 1, along with suggested usage.4-6

Sources of Evidence on Harms

Randomized Controlled Trials

Published trials. Properly designed and executed randomized controlled trials (RCTs) are considered the "gold standard" for evaluating efficacy because they minimize potential bias. However, relying solely on published RCTs to evaluate harms in CERs is problematic. First, most RCTs lack prespecified hypotheses for harms.5 Rather, hypotheses are usually designed to evaluate beneficial effects, with assessment of harms a secondary consideration. As such, the quality and quantity of harms reporting in clinical trials is frequently inadequate.7,8

Second, few RCTs have large enough sample sizes or are long enough in duration to adequately assess uncommon or long-term harms.9

Third, most RCTs are explanatory, rather than pragmatic, in design—i.e., they assess benefits and harms in ideal, homogeneous populations and settings.10 Patients who are more susceptible to adverse events are often underrepresented in such "efficacy" trials. Even when harms are appropriately assessed and reported, the applicability of efficacy trials to general practice is limited.

Fourth, relatively few RCTs directly compare alternative treatment strategies. Although CER authors can evaluate benefits or harms of two competing interventions based on trials in which each is compared with a common third treatment (usually placebo), the results of indirect comparisons do not always agree with direct comparisons.11,12

Fifth, publication and selective outcome(s) reporting bias can lead to distorted conclusions about harms when data are unpublished, partially reported, downplayed, or omitted.13,14

Finally, in some cases, RCTs may not be available. For example, surgical procedures and medical devices often become widely disseminated with few or no randomized trial data. The same can be true for older therapeutic devices, such as hyperbaric oxygen chambers.15

Despite these limitations, RCTs are the gold standard for demonstrating efficacy, the basis for most regulatory approvals, and the source of most advertising and other claims made on behalf of drugs and other interventions. For this reason, CERs must address harms data from RCTs in detail when they are available.

"Head-to-head" RCTs provide the most direct evidence on comparative harms. However, placebo-controlled RCTs may also provide important information on absolute and relative risks and contribute to more precise estimates of harms. In addition, placebo-controlled trials can provide information about risks that may not be apparent from head-to-head trials. For example, a systematic review of nonsteroidal anti-inflammatory drugs (NSAIDs) found cyclo-oxygenase-2 selective NSAIDs associated with greater myocardial risk vs. placebo, but differences were not apparent vs. nonselective NSAIDs, which were also associated with increased risk.16 In general, CERs should routinely include placebo-controlled trials for assessment of harms, particularly for rare or uncommon adverse events. In lieu of examining individual placebo controlled trials, CERs may incorporate findings of well-conducted systematic reviews, provided they evaluate the specific harms of interest.

Unpublished supplemental trials data. In addition to evaluating results of published RCTs, CER authors should consider including results of completed or terminated but unpublished RCTs, as well as unpublished results from published trials. Such information has several potentially valuable uses:

  • To assess the number of unpublished trials or frequency of unreported outcomes, which can help in evaluating risk for publication or outcomes reporting bias.
  • To evaluate whether conclusions based on unpublished data are qualitatively different from those based on published RCTs.
  • To conduct formal quantitative meta-analysis, including published and unpublished RCTs or outcomes.

Unpublished clinical trials tend to report lower estimates of treatment benefits than published trials (i.e., weaker intervention effects).17,18 The impact of unpublished trials on assessments of harms has not been extensively studied, but a systematic review of antidepressants in children found that addition of data from unpublished trials changed conclusions about the balance of risks and benefits from favorable to unfavorable for several drugs.19

Data from unpublished trials can be difficult to locate systematically. At a minimum, material from the FDA Web site should routinely be examined in order to assess what effect unpublished (completed or terminated) trials submitted for regulatory approval may have on conclusions regarding harms. In addition, starting in 2009, trial sponsors are required by the 2007 FDA reform bill to report results to a clinical trial results database (www.ClinicalTrials.gov).20 Other resources for identifying unpublished trials include obtaining information from non-U.S. regulatory agencies and directly querying funding sources. Once unpublished trials are located, two caveats should also be considered. Frequently, there is insufficient information from unpublished trials to assess fully the risk of bias. Also, the results and conclusions of trials may change between initial presentation of data and publication in a peer-reviewed journal.21

Even when a trial is published, important information may be omitted because of space limitations or other reasons.22,23 For example, before the publication of the Vioxx Gastrointestinal Outcomes Research Study (VIGOR) in 2001,24 information on myocardial infarctions was absent from most published reports of trials evaluating selective or nonselective NSAIDs because an association with cardiovascular events was not suspected. A systematic review that obtained unpublished myocardial infarction data from older trials found an increased risk with high doses of all evaluated NSAIDs (selective or nonselective) other than naproxen.16 An analysis of myocardial infarction risk based on only published information would have been seriously compromised by incomplete data.

Drug approval information—for example, the clinical and statistical reviews prepared by staff of the FDA—frequently provides details about harms not included in journal publications. For example, the Celecoxib Long-term Arthritis Safety Study (CLASS), a major trial of celecoxib, was published in the Journal of the American Medical Association as a 6-month study and reported fewer gastrointestinal adverse events for celecoxib than for two nonselective NSAID comparators.25 The JAMA article did not mention that some patients in the trial had been observed for longer than 6 months.26 In contrast, the FDA review reported all the outcomes data, including data that showed no difference in gastrointestinal adverse events at the end of followup.27

Limited evidence suggests an inverse relationship between the proportion of included trials reporting a specific outcome and the estimates of treatment benefit for that outcome, possibly due to selective reporting of favorable outcomes.28 How the proportion of included trials reporting outcomes affects estimates of harms has not been well studied. Nonetheless, when a significant proportion of published trials fail to report an important or critical adverse event, CER authors should report on this gap in the evidence and consider efforts to obtain unpublished data (e.g., by querying study authors, funding sources, or clinical trials results databases, or performing more detailed reviews of FDA documents).

Observational Studies

Observational studies are almost always necessary to assess harms adequately. The exception is when there are sufficient data from RCTs to reliably estimate harms. However, even though observational studies are more susceptible to bias than well-conducted RCTs, for some comparisons there may be few or no long-term, large, head-to-head, or effectiveness RCTs.29 Observational studies may also provide the best (or only) evidence for evaluating harms in minority or vulnerable populations (such as pregnant women, children, elderly patients, or those with multiple comorbidities) who are underrepresented in clinical trials.

The term "observational studies" is commonly used to refer to cohort, case-control, and cross-sectional studies,30 but can refer to a broad range of study designs, including case reports, uncontrolled series of patients receiving surgery or other interventions, and others.31 All can yield useful information as long as their specific limitations are understood.

The types of observational studies included in a CER will vary depending on the type or frequency of adverse events being evaluated. The choice of study designs also depends on whether investigators are seeking to determine what harms might be associated with a treatment (hypothesis generating) or whether certain harms are more likely (hypothesis testing). Different types of observational studies might be included or rendered irrelevant by availability of data from stronger study types.

Cohort and case-control studies. CER authors should routinely search for and include well-designed and reported case-control and population-based cohort studies.30,32 Such studies are well suited for testing hypotheses on whether one intervention is associated with a greater risk for an adverse event than is another and for quantifying the risk. They also take stronger precautions against bias than do other observational designs, and their strengths and weaknesses are well understood. For unexpected adverse events, for example, confounding by indication may not be as important an issue in case-control and cohort studies as when evaluating beneficial effects because their occurrence is usually not associated with the reasons for choosing a particular treatment.29,33 Although cross-sectional studies have features in common with cohort studies, it is difficult to establish causality because exposures, and outcomes are evaluated simultaneously. Indeed, associations in cross-sectional studies may sometimes be due to reverse causality.34

A recent report found that large observational studies usually report smaller absolute risks of harm than do large randomized trials.35 There was no clear tendency for randomized trials or observational studies to report larger relative risks. In more than one-half of the comparisons assessed, estimates of relative or absolute risk varied more than twofold. Discrepancies between randomized trials and observational studies may occur because of differences in populations, settings, or interventions; differences in study design, including criteria used to identify harms; differential effects of biases, or some combination of these factors.

Observational studies based on patient registries. Patient registries collect information on clinical outcomes in populations defined by a particular disease, condition, or exposure.36 Clinical data are prospectively collected for specific research purposes using active methods to identify outcomes, although registry information can be supplemented by information from administrative databases and other sources. Registries can be designed as an active surveillance system for identifying harms and may be particularly useful for assessing long-term or uncommon adverse events.

Observational studies based on analyses of large databases. Pharmacoepidemiologic studies using large databases to identify exposures and outcomes may be valuable for comparing the risk of uncommon adverse events.37 However, additional empirical research is needed to identify methods for collecting and analyzing data in pharmacoepidemiologic studies that are associated with valid findings.38 Unlike studies based on patient registries, large administrative databases usually contain information routinely collected during clinic, hospital, laboratory, or pharmacy encounters, rather than for a specific research purpose. Such studies are probably most useful for evaluating serious harms that are more reliably reported and recorded (for example, death or acute myocardial infarction) than less serious harms that may not generate a specific clinic visit or diagnostic code (for example, sedation or nausea). In some cases, administrative data may be supplemented or verified by more detailed clinical information. Regardless of how data are obtained, all observational studies should employ appropriate methods for minimizing bias and misclassification of data.

Case reports and postmarketing surveillance. About 30 percent of the primary published literature on adverse drug events is in the form of case reports.39 Case reports can be useful for identifying uncommon, unexpected, or long-term adverse events, particularly for new drugs or other interventions.40 The adverse events identified by case reports often differ from those detected in clinical trials.41 However, case reports are usually considered to be hypothesis generating because it is difficult to calculate information from them about the frequency or comparative risk of adverse events.

In the United States, the FDA receives about 280,000 reports of postmarketing adverse events annually, collects them into a database,42 and issues information about adverse drug events on its MedWatch Web site (http://www.fda.gov/medwatch/). Although pharmaceutical companies and other investigators may also perform passive surveillance of harms on postmarketing data, such analyses are not always made public in a timely fashion.43 Active, hypothesis-driven postmarketing surveillance systems have been developed recently for identifying and evaluating serious adverse drug events.44.

Case reports and other hypothesis-generating studies may be useful for CERs evaluating new drugs suspected of being associated with serious but uncommon adverse events. For other topics, CER authors may consider their inclusion on a case-by-case basis.

Other observational studies. Several other types of observational studies may also report data on harms. However, they are likely to be more prone to bias than RCTs or well-designed case-control or cohort studies, and their use needs to be considered cautiously. For example, studies reporting harms from surgical or other invasive interventions often consist of a series of patients who received the procedure. Data are often insufficient to assess the methods used to select participants.45 In addition, because such studies lack control groups, evaluating effects of confounding is difficult, as is comparing risks of adverse events across interventions.

Other quasi-experimental study designs may not offer any advantage over RCTs in terms of their applicability to routine practice. For example, open-label extensions of clinical trials may follow patients for an extended period of time, but they usually enroll a more highly selected population (patients who completed the randomized trial, tolerated the medication, and agreed to participate in the extension), are unblinded, and often lack a comparison arm. Such studies can be excluded from CERs if more reliable long-term, comparative data are available. If they are included in CERs, their limitations should be described clearly.

Criteria to select observational studies for inclusion. In general, many more observational studies than randomized trials will be available for nearly all health care interventions. Evaluating a large number of observational studies can be impractical when conducting a CER, especially when a significant proportion either do not add useful information or carry a high risk of reporting biased results.

Several criteria have commonly been used in systematic reviews and CERs to screen observational studies of harms for inclusion. Empirical data are lacking on how use of different selection criteria affects estimates of harms. However, CERs should match inclusion criteria to the reasons for including observational studies. For example, inclusion criteria might specify minimum duration of followup if a priority is to identify evidence on long-term harms. If large, higher quality studies are available, it could be reasonable to specify a minimum sample size threshold in order to utilize resources efficiently. Methods sections should clearly describe selection criteria along with the rationale for choosing the criteria. Commonly used inclusion criteria for observational studies are shown in Table 2.

Assessing Risk of Bias (Quality) of Harms Reporting

Randomized Trials

A number of features of RCTs have been empirically tested and proposed as markers of higher quality (i.e., lower risk of bias). These include use of appropriate randomization generation and allocation concealment techniques; blinding of participants, health care providers, and outcomes assessors; and analysis according to intention-to-treat principles.46 Whether these are equally important in protecting against bias in studies reporting harms is unclear. Moreover, because evaluating harms is often a secondary consideration in randomized trials, the quality of harms assessment and reporting can be inadequate even when assessment of the primary (beneficial) outcome is appropriate.

When evaluating the quality of harms assessment, CER authors should consider whether adequate methods were used to identify adverse events in the primary studies. Active methods, such as querying patients using a comprehensive checklist or standardized laboratory tests, are more likely to completely identify adverse events than passive methods, such as relying on patient self-report.47 In addition, specific data on adverse events are likely to be more accurate and informative than generic statements, such as "no adverse events were noted" or "the interventions were well tolerated." If a specific adverse event is not reported, it is generally safer for CER authors to assume that they were not ascertained or not recorded than to assume that the prevalence or incidence was zero.4

It is also important to assess how adverse events are assessed and categorized. Studies should predefine the qualifiers "serious" and "severe" to describe adverse events. Otherwise, it is impossible for readers to determine whether these labels were applied consistently within and across trials. Standardized criteria for grading severity of adverse events are available for certain conditions.48,49 CERs should note when grading severity or seriousness of adverse events is based on nonstandardized or poorly defined criteria, as such classifications may not be comparable across studies or may be poorly reproducible. Similarly, methods for classifying adverse events as "treatment related" are largely subjective, with unknown validity, and such data may be particularly unreliable.

It is not always necessary for trials to prespecify or define adverse events. For example, studies reporting unexpected outcomes can be very valuable for identifying previously unrecognized harms. However, when evaluating known harms, using validated or standardized criteria for adverse events may help reduce subjectivity or bias in their assessment and classification. In drug trials, use of an independent external endpoint committee may provide less biased estimates of harms than outcomes assessment performed by investigators connected to the study.50

"Withdrawals due to adverse events" are commonly reported in trials, and they are often used in systematic reviews as a marker for intolerable or severe adverse events. However, the Cochrane Adverse Effects Methods Group suggests caution in interpreting withdrawals attributed to adverse events in this manner, for the following reasons:4

  • Attribution of reasons for discontinuation is likely to be imprecise and to vary across trials.
  • Pressure to keep dropouts low in trials may result in rates that do not reflect real-world practice.
  • Unblinding often takes place before the decision to withdraw, which can lead to distortion of estimates of an intervention's effect on withdrawal (e.g., symptoms are less likely to lead to withdrawal if the patient is found to be on placebo).

Nonetheless, withdrawals due to adverse events are often reported even when serious or severe adverse events are not reported or are poorly defined, and they may provide some useful information.

Observational Studies

Because observational studies lack randomization, they should adhere to high methodological standards to be considered valid.30,32,51 RCTs are expected to have outcomes recorded by blinded personnel and to include all participants who were randomized in the analysis of results. Use of blinded outcome assessors and a clearly identified inception cohort (e.g., "new users")52 is at least as important when assessing observational studies.

Instruments for assessing risk of bias in observational studies vary greatly in scope, number and types of items used, and developmental rigor.53 Further study is needed to determine which methodological shortcomings in observational studies are consistently associated with bias in assessment and reporting of harms. However, some consensus exists on the major domains that should be considered when evaluating the overall validity of an observational study. For cohort studies, important factors include assembly of an inception cohort, complete followup, appropriate assessment of potential confounders, accurate determination of exposures and outcomes, and blinded assessment of outcomes.30,52-54

Several studies have empirically evaluated effects of specific methodological characteristics on estimates of harms from observational studies. They found that prospective or retrospective design,55,56 case-control compared with cohort studies57,58 and smaller compared with larger case series55 did not have consistent effects on estimates of harms. Two studies found that industry-funded studies tended to report more favorable outcomes than did studies with other funding sources.57,59 Because all of these studies evaluated fairly limited samples of studies, their wider applicability is uncertain.

Observational studies based on evaluations of large administrative databases should follow the same general principles to reduce bias as observational studies that directly collect data from patients. In these cases, reviewers should pay particular attention to the methods used for ascertaining exposures and outcomes and for measuring and analyzing potential confounders, as these issues are more likely to be problematic in studies relying on administrative claims (although not unique to them).37

For all observational study designs, estimates of harms are less likely to be confounded when evaluating previously unsuspected adverse events than when evaluating a known harm or intended effects. For example, the finding that cyclo-oxygenase-2-selective NSAIDS were associated with an increased risk of myocardial infarction vs. nonselective NSAIDs was an unexpected finding from an RCT examining a different outcome.24 This risk could be confirmed in observational studies, in part because the choice of type of NSAID in typical practice was unrelated to the patients' risk for myocardial infarction. In contrast, gastrointestinal bleeding was a known risk of nonselective NSAIDS, and clinicians were more likely to prescribe selective NSAIDs in patients at higher risk for gastrointestinal bleeding. Such "confounding by indication" led to the appearance of an apparent association between selective NSAID use and bleeding in epidemiologic studies.60 In some cases, such spurious associations may remain despite adjustment for known confounders ("residual confounding").

Uncontrolled Studies

Studies of surgery, medical devices, and other nonpharmacologic interventions are often uncontrolled series of patients who received the therapy and then were followed over a period of time. Such studies can provide some information about rates of adverse events in clinical practice, and they may be most informative when the incidence of such events in untreated patients is low. Unfortunately, such studies frequently do not meet standards for accurate and comprehensive reporting of harms.61 Even when harms data are well described, an important limitation of uncontrolled studies is that it is difficult to evaluate confounding by indication. Authors are also more likely to submit for publication studies showing the best outcomes.

For some interventions, CER authors must consider including uncontrolled studies for assessing harms, as little or no other evidence may be available. Proposed criteria for evaluating case series are likely to promote improved reporting of results,62 but may provide only limited information about risk of bias. Important factors to consider when evaluating uncontrolled studies include whether the study enrolled or attempted to enroll all patients meeting prespecified inclusion criteria and whether the study clearly describes loss to followup.45 When uncontrolled studies do not meet these criteria, determining the reliability and applicability of even well-described results may be impossible.

Instruments for Assessing Risk of Bias (Quality) in Studies on Harms

Development of instruments for assessing risk of bias specifically in studies of harms is still in an early stage of development. Two issues remain unclear: whether to use a specific rating instrument to evaluate harms assessment and reporting, or whether using instruments for rating the overall risk of bias of a study is sufficient, as long as particular attention is paid to how well adverse events are defined, ascertained, and reported.

Chou et al. empirically developed and tested an instrument for assessing quality of harms assessment and reporting in randomized trials and observational studies of carotid endarterectomy for symptomatic carotid artery stenosis.63 This approach involved four criteria: nonbiased selection of subjects, low loss to followup, adverse events prespecified and defined, and adequate duration of followup. Studies meeting at least three of the four criteria reported a rate of postsurgical complications of 5.7 percent (95-percent confidence interval [CI], 4.8 percent to 6.6 percent), compared with 3.7 percent (95-percent CI, 3.1 percent to 4.3 percent) for studies meeting fewer than three of the criteria. However, the generalizability of this instrument to other datasets or interventions is unclear. When the authors applied these criteria to studies of rofecoxib, they were unable to show differences in estimates of risk of myocardial infarction. In addition, caution should be used when considering use of summary scores to assess risk of bias.64 At a minimum, key methodological aspects should be assessed individually and their influence on estimates of harms explored.

Santaguida et al. have also developed a quality-rating instrument (McHarm) for evaluating studies reporting harms (Table 3).65 The tool was developed from quality rating items generated by a review of the literature on harms and from previous quality assessment instruments. A formal Delphi consensus exercise was used to reduce the number of items. The subsequent list of quality criteria specific to harms was tested for reliability and face, construct, and criterion validity. This quality-assessment tool is intended for use in conjunction with standardized quality-assessment tools for design-specific internal validity issues.

Case reports may provide valuable information about the possibility of rare or previously unrecognized adverse events. A 1982 study examined 47 case reports published in 1963 in four major general medical journals and judged that 35 of them were subsequently proved to be "clearly" correct.66 However, the methods used to determine reliability of case reports in this study were subjective, and results have not been replicated. A recent study, in fact, found that only 18 percent of case reports of suspected adverse drug reactions have been subjected to rigorous evaluation in subsequent studies.67 Nonetheless, statistical modeling study suggests that the likelihood of more than one to three spontaneously reported cases is very unlikely to be coincidental when the adverse event is rare or uncommon.68 Case reports, however, cannot be used to estimate the rate of an adverse event, which may be critical to any decisions.

Several disease-specific69 and non-disease-specific70 methods for assessing the probability of causality from case reports of adverse events have been developed. These methods represent expert opinion and have not been validated empirically. Factors believed to increase the likelihood of causality are shown in Table 4.69,70

Guidelines for improving the reporting of suspected adverse drug events in case reports have also recently been proposed.71 In 35 reports of 48 patients published in the British Medical Journal, the median number of recommended items that were reported was 9 of 19 (range 5-12), although effects of missing information on the validity of case reports have not been studied.

Synthesizing Evidence on Harms

CER authors should follow general principles for synthesizing evidence when evaluating data on harms. Such principles include: combining studies only when they are similar enough to warrant combining;72 adequately considering risk of bias, including publication and other related biases;73 and exploring potential sources of heterogeneity.23 Several other issues are especially relevant for synthesizing evidence on harms.

Uncommon or Rare Adverse Events

Evaluating comparative risks of uncommon or rare adverse events in CERs can be particularly challenging. A frequent problem in RCTs and systematic reviews is interpreting a nonsignificant probability value as indicating no difference in risk for rare adverse events, particularly when the confidence intervals are wide and encompass the possibility of clinically important risks.74,75 For example, one trial concluded that, in patients with meningitis, "treatment with dexamethasone did not result in an increased risk of adverse events" compared with placebo for treatment of hyperglycemia, herpes zoster, or fungal infection because P values for all three outcomes were more than 0.20.76 However, the 95-percent confidence intervals for estimates of relative risks for these three adverse events encompassed clinically significant increases in risk (-13.5 percent to 77.6 percent, -60.4 percent to 377.7 percent, and -43.6 percent to 496.2 percent, respectively). In such cases, CERs should acknowledge the lack of statistical power to assess risk adequately and should interpret the confidence intervals, including the possibility or probability of excess harm.

Equivalence and Noninferiority

CER authors should draw conclusions about "equivalence" or "noninferiority" of interventions with regard to harms only when there are appropriate data to justify such statements.77 Few CERs will have the statistical power to adequately assess noninferiority when the risk of an adverse event is on the order of 1 percent or lower. For example, about 100,000 patients would have been needed in the COBALT or GUSTOIII trials to rule out an excess relative death rate of 5 percent from alternative thrombolytic agents with 80-percent power.78 Ruling out smaller event rates would require even higher sample sizes.

Indirect Analyses

Placebo-controlled trials can be helpful for evaluating absolute risks associated with an intervention. When head-to-head trials are sparse or unavailable, placebo-controlled trials may also be useful for indirectly evaluating comparative harms, particularly for rare or uncommon adverse events. However, for indirect analyses to be reliable, all studies should be comparable in terms of quality, factors related to applicability (population, dosing, co-interventions, and settings), measurement of outcomes, and incidence of adverse events in control groups.12,79

For example, a meta-analysis found that rofecoxib was associated with an increased risk of arrhythmia compared with control treatments; celecoxib was not.80 However, the rate of arrhythmia in the control arms was tenfold higher in trials of celecoxib (0.27 percent, or 18 of 6,568 subjects) than in trials of rofecoxib (0.02 percent, or 2 of 10,174 subjects). In this situation, indirect comparisons about the relative safety of celecoxib compared with rofecoxib are likely to be problematic. A more informative approach would be to explore reasons for the discrepancies in rates of arrhythmias in the control arms and how they may have affected comparisons.

More studies are needed to determine when indirect comparisons are most likely to be valid. In the meantime, CER authors considering indirect analyses to assess harms should carefully consider whether assumptions underlying valid indirect comparisons are likely to be met, compare results of indirect comparisons with head-to-head data if available, and draw conclusions from indirect comparisons cautiously.

Combining Data From Different Types Of Studies

Most CERs will include data on harms from different types of studies. Statistical combination of data from observational studies is often inappropriate and should be avoided unless there is a clear rationale to do so.81 If such analyses are undertaken, the justification should be clearly explained.

Discrepancies Between Randomized Trials and Observational Studies

A separate challenging situation occurs when results on harms from randomized trials and observational studies are discordant. Some reasons for discrepancies between randomized trials and observational studies are shown in Table 5. A reasoned analysis of potential sources of discrepancy is generally more helpful than simply presenting the different results.

Reporting Evidence on Harms

As when reporting evidence on benefits, CERs should emphasize the most reliable information for the most important adverse events. Summary tables should generally present data for the most important harms first, with more reliable evidence preceding less reliable evidence. Evidence on harms from each type of study should be clearly summarized in summary tables, narrative format, or both.2 A critical role of CERs is to report clearly on the limitations of the evidence on harms and to analyze and interpret thoughtfully how these limitations may affect estimates of the balance of benefit and harm. Suggested elements to focus on when reporting harms are shown in Table 6.

Summary

A summary of the key points about assessment of harms discussed in this report is shown in Table 7.

Acknowledgments

The authors would like to acknowledge Gail R. Janes for participating in the workgroup calls.

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Tables

Table 1. Terminology for reporting on harms

Active surveillance of harms
Participants are asked in structured questionnaires or interviews about the occurrence of specific adverse events, or predefined laboratory or other diagnostic tests are performed at prespecified time intervals.
Adverse effect
A harmful or undesirable outcome that occurs during or after the use of a drug or intervention for which there is at least a reasonable possibility of a causal relation.
Adverse event
A harmful or undesirable outcome that occurs during or after the use of a drug or intervention but is not necessarily caused by it. When causality is uncertain or the purpose of the Comparative Effectiveness Review is to establish causality, "adverse event" should generally be the default term over "adverse effect" or "adverse reaction/adverse drug reaction."
Adverse reaction/adverse drug reaction
An adverse effect specifically associated with a drug.
Complications
A term often used to describe adverse events following surgery or other invasive interventions.
Harms
The totality of all possible adverse consequences of an intervention.
Passive surveillance of harms
Participants are not specifically asked about or tested for the occurrence of adverse events. Rather, adverse events are identified based on patient reports made on their own initiative.
Risk-benefit ratio
A common expression for the comparison of overall harms and benefits. However, because benefits and harms of an intervention are usually very different in character and are measured on different scales, a true "risk-benefit ratio" is rarely calculable. In addition, there may be several distinct benefits and harms. A preferred term is "balance of benefits and harms."
Safety
Substantive evidence of an absence of harm. Do not use this term (or the term "safe") when evidence on harms is simply absent or insufficient.
Serious adverse event
Any adverse event with serious medical consequences, including death, hospital admission, prolonged hospitalization, and persistent or significant disability or incapacity.
Severe adverse event
An adverse event whose intensity is considered severe (including "nonserious" adverse events). For example, a rash could be "severe" but not "serious" (i.e., not resulting in death, hospital admission, prolonged hospitalization, or persistent or significant disability).
Side effects
Unintended drug effects (beneficial or harmful) given at doses normally used for therapeutic effects. Use of this term may tend to understate the important of harms because the word "side" may be perceived to suggest secondary importance.
Tolerability
This term is often used imprecisely but should be used to refer to a patient's or subject's ability or willingness to tolerate or accept unpleasant drug-related adverse events without serious or permanent sequelae.
Toxicity
The term "toxicity" is used in pharmacology and microbiology to refer to the quality of being poisonous, especially the degree of virulence of a toxic microbe or of a poison. It is often measured in terms of the specific target affected (e.g., cytotoxicity or hepatotoxicity). In the context of systematic reviews, the term is often used to refer to laboratory-determined abnormalities, such as elevated liver function tests. However, the terms "abnormal laboratory measurements" and "laboratory abnormalities" are more specific and appropriate.

Table 2. Example criteria for selecting observational studies on harms for inclusion in a Comparative Effectiveness Review

  • Studies meet certain study design definitions (e.g., cohort and case-control studies)
  • Studies do not exceed a defined threshold for risk of bias (e.g., studies assessed as being at low risk of bias or meeting certain prespecified quality criteria)
  • Studies meet a defined threshold for duration of followup
  • Studies meet a sample size threshold
  • Studies evaluate a specific population of interest (e.g., studies evaluating populations underrepresented in randomized trials, such as elderly, women, or minority populations)

Table 3. McMaster tool for assessing quality of harms assessment and reporting in study reports (McHarm)

  1. Were the harms PRE-DEFINED using standardized or precise definitions?
  2. Were SERIOUS events precisely defined?
  3. Were SEVERE events precisely defined?
  4. Were the number of DEATHS in each study group specified OR were the reason(s) for not specifying them given?
  5. Was the mode of harms collection specified as ACTIVE?
  6. Was the mode of harms collection specified as PASSIVE?
  7. Did the study specify WHO collected the harms?
  8. Did the study specify the TRAINING or BACKGROUND of who ascertained the harms?
  9. Did the study specify the TIMING and FREQUENCY of collection of the harms?
  10. Did the author(s) use STANDARD scale(s) or checklist(s) for harms collection?
  11. Did the authors specify if the harms reported encompass ALL the events collected or a selected SAMPLE?
  12. Was the NUMBER of participants that withdrew or were lost to follow-up specified for each study group?
  13. Was the TOTAL NUMBER of participants affected by harms specified for each study arm?
  14. Did the author(s) specify the NUMBER for each TYPE of harmful event for each study group?
  15. Did the author(s) specify the type of analyses undertaken for harms data

Source: Santaguida PL, Raina P. The development of the McHarm quality assessment scale for adverse events: Delphi consensus on important criteria for evaluating harms. http://hiru.mcmaster.ca/epc/mcharm.pdf. Accessed May 14, 2008.

Table 4. Criteria for evaluating the likelihood of a causal relationship in case reports

  • Temporal relationship (exposure preceding adverse event and adverse event appearing at an appropriate time interval after exposure)
  • Lack of alternative causes
  • Drug levels in body fluids or tissues
  • Resolution or improvement after discontinuation
  • Dose-response relationship
  • Recurrence following rechallenge (that is, restarting the drug to see whether the adverse reaction recurs)
  • Confirmation of adverse event by objective information

Table 5. Sources of discrepancy between randomized controlled trials and observational studies

  • Differences in risk of bias (study quality)
  • Differences in applicability (study populations, interventions, or settings)
  • Differences in methods used to define or measure outcomes
  • Differential effects of publication or selective outcomes reporting bias
  • Differential effects related to funding source (observational studies less likely to be funded by industry)

Table 6. Elements to report when describing results for harms in Comparative Effectiveness Reviews
Element Factors
Risk of bias (quality) Study design, number of studies, study quality, consistency of evidence, directness of evidence, other modifying factors
Applicability Population characteristics, interventions, co-interventions, comparisons, outcomes, duration of followup for various harms
Results Number of patients, absolute and relative estimates of risks
Publication bias or incomplete outcomes data Graphic and/or statistical assessments for publication bias, known unpublished studies, number of studies not reporting key harms
Additional analyses Sensitivity analyses, subgroup analyses, metaregression, etc.

Table 7. Summary of key points on assessment of harms in Comparative Effectiveness Reviews

  • Assess all important harms, whenever possible.
  • Use multiple sources of information, including clinical experts and stakeholders, to identify important harms.
  • Use consistent and precise terminology when reporting data on harms, and avoid terms implying causality unless causality is reasonably certain.
  • Gather evidence on harms from a broad range of sources, including observational studies, particularly when clinical trials are lacking; when generalizability is uncertain; or when investigating rare, long-term, or unexpected harms.
  • Do not assume studies adequately assess harms because methods used to assess and report benefits are appropriate; rather, evaluate how well studies identify and analyze harms.
  • Be cautious about drawing conclusions on harms when events are rare and estimates of risk are imprecise.
  • Include placebo-controlled trials, particularly for assessing uncommon or rare harms, but be cautious about relying on indirect comparisons to judge comparative risks, and evaluate whether studies being considered for indirect comparisons meet assumptions for consistency of treatment effects.
  • Avoid inappropriate combining of data on harms, and thoroughly investigate inconsistent results.
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