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A tool for empirical equipoise assessment in multigroup comparative effectiveness research.

ABSTRACT: PURPOSE:In observational research, equipoise concerns whether groups being compared are similar enough for valid inference. Empirical equipoise was previously proposed as a tool to assess patient similarity based on propensity scores (PS). We extended this work for multigroup observational studies. METHODS:We modified the tool to allow for multinomial exposures such that the proposed definition reduces to the original when there are only two groups. We illustrated how the tool can be used as a method to assess study design within three-group clinical examples. We then conducted three-group simulations to assess how the tool performed in a setting with residual confounding after PS weighting. RESULTS:In a clinical example based on rheumatoid arthritis, 44.5% of the sample fell within the region of empirical equipoise when considering first-line biologics, whereas 57.7% did so for second-line biologics, consistent with the expectation that a second-line design results in better equipoise. In a simulation where the unmeasured confounder had the same magnitude of association with the treatment as the measured confounders and a 25% greater association with the outcome, the tool crossed the proposed threshold for empirical equipoise at a residual confounding of 20% on the ratio scale. When the unmeasured variable had a twice larger association with treatment, the tool became less sensitive and crossed the threshold at a residual confounding of 30%. CONCLUSION:Our proposed tool may be useful in guiding cohort identification in multigroup observational studies, particularly with similar effects of unmeasured and measured covariates on treatment and outcome.

SUBMITTER: Yoshida K

PROVIDER: S-EPMC7057252 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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A tool for empirical equipoise assessment in multigroup comparative effectiveness research.

Yoshida Kazuki K Solomon Daniel H DH Haneuse Sebastien S Kim Seoyoung C SC Patorno Elisabetta E Tedeschi Sara K SK Lyu Houchen H Hernández-Díaz Sonia S Glynn Robert J RJ

Pharmacoepidemiology and drug safety 20190527 7

<h4>Purpose</h4>In observational research, equipoise concerns whether groups being compared are similar enough for valid inference. Empirical equipoise was previously proposed as a tool to assess patient similarity based on propensity scores (PS). We extended this work for multigroup observational studies.<h4>Methods</h4>We modified the tool to allow for multinomial exposures such that the proposed definition reduces to the original when there are only two groups. We illustrated how the tool can ...[more]

PMID: 31131965

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Project description:BackgroundThe $1.1 billion investment in comparative effectiveness research will reshape the evidence-base supporting decisions about treatment effectiveness, safety, and cost. Defining the current prevalence and characteristics of comparative effectiveness (CE) research will enable future assessments of the impact of this program.MethodsWe conducted an observational study of clinical trials addressing priority research topics defined by the Institute of Medicine and conducted in the US between 2007 and 2010. Trials were identified in ClinicalTrials.gov. Main outcome measures were the prevalence of comparative effectiveness research, nature of comparators selected, funding sources, and impact of these factors on results.Results231 (22.3%; 95% CI 19.8%-24.9%) studies were CE studies and 804 (77.7%; 95% CI, 75.1%-80.2%) were non-CE studies, with 379 (36.6%; 95% CI, 33.7%-39.6%) employing a placebo control and 425 (41.1%; 95% CI, 38.1%-44.1%) no control. The most common treatments examined in CE studies were drug interventions (37.2%), behavioral interventions (28.6%), and procedures (15.6%). Study findings were favorable for the experimental treatment in 34.8% of CE studies and greater than twice as many (78.6%) non-CE studies (P<0.001). CE studies were more likely to receive government funding (P?=?0.003) and less likely to receive industry funding (P?=?0.01), with 71.8% of CE studies primarily funded by a noncommercial source. The types of interventions studied differed based on funding source, with 95.4% of industry trials studying a drug or device. In addition, industry-funded CE studies were associated with the fewest pediatric subjects (P<0.001), the largest anticipated sample size (P<0.001), and the shortest study duration (P<0.001).ConclusionsIn this sample of studies examining high priority areas for CE research, less than a quarter are CE studies and the majority is supported by government and nonprofits. The low prevalence of CE research exists across CE studies with a broad array of interventions and characteristics.

Project description:ObjectiveTo examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) ("living" network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis.DesignEmpirical study of the accumulating evidence about the comparative effectiveness of clinical interventions.Data sourcesDatabase of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015.Eligibility criteria for study selectionNetwork meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P<0.10).Outcomes and analysisCumulative pairwise and network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses.Results49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis.ConclusionsIn comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses.

Project description:BackgroundEvidence from observational comparative effectiveness research (CER) is ranked below that from randomized controlled trials in traditional evidence hierarchies. However, asthma observational CER studies represent an important complementary evidence source answering different research questions and are particularly valuable in guiding clinical decision making in real-life patient and practice settings. Tools are required to assist in quality appraisal of observational CER to enable identification of and confidence in high-quality CER evidence to inform guideline development.MethodsThe REal Life EVidence AssessmeNt Tool (RELEVANT) was developed through a step-wise approach. We conducted an iterative refinement of the tool based on Task Force member expertise and feedback from pilot testing the tool until reaching adequate inter-rater agreement percentages. Two distinct pilots were conducted-the first involving six members of the Respiratory Effectiveness Group (REG) and European Academy of Allergy and Clinical Immunology (EAACI) joint Task Force for quality appraisal of observational asthma CER; the second involving 22 members of REG and EAACI membership. The final tool consists of 21 quality sub-items distributed across seven methodology domains: Background, Design, Measures, Analysis, Results, Discussion/Interpretation, and Conflict of Interest. Eleven of these sub-items are considered critical and named "primary sub-items".ResultsFollowing the second pilot, RELEVANT showed inter-rater agreement ??70% for 94% of all primary and 93% for all secondary sub-items tested across three rater groups. For observational CER to be classified as sufficiently high quality for future guideline consideration, all RELEVANT primary sub-items must be fulfilled. The ten secondary sub-items further qualify the relative strengths and weaknesses of the published CER evidence. RELEVANT could also be applicable to general quality appraisal of observational CER across other medical specialties.ConclusionsRELEVANT is the first quality checklist to assist in the appraisal of published observational CER developed through iterative feedback derived from pilot implementation and inter-rater agreement evaluation. Developed for a REG-EAACI Task Force quality appraisal of recent asthma CER, RELEVANT also has wider utility to support appraisal of CER literature in general (including pre-publication). It may also assist in manuscript development and in educating relevant stakeholders about key quality markers in observational CER.

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A tool for empirical equipoise assessment in multigroup comparative effectiveness research.

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A tool for empirical equipoise assessment in multigroup comparative effectiveness research.

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