Dataset Information

Intervention effect estimates in cluster randomized versus individually randomized trials: a meta-epidemiological study.

ABSTRACT:

Background

Cluster randomized trials (CRTs) and individually randomized trials (IRTs) are often pooled together in meta-analyses (MAs) of randomized trials. However, the potential systematic differences in intervention effect estimates between these two trial types has never been investigated. Therefore, we conducted a meta-epidemiological study comparing intervention effect estimates between CRTs and IRTs.

Methods

All Cochrane MAs including at least one CRT and one IRT, published between 1 January 2010 and 31 December 2014, were included. For each MA, we estimated a ratio of odds ratios (ROR) for binary outcomes or a difference of standardized differences (DSMD) for continuous outcomes, where less than 1 (or 0, respectively) indicated a greater intervention effect estimate with CRTs.

Results

Among 1301 screened reviews, we selected 121 MAs, of which 76 had a binary outcome and 45 had a continuous outcome. For binary outcomes, intervention effect estimates did not differ between CRTs and IRTs [ROR 1.00, 95% confidence interval (0.93 to 1.08)]. Subgroup and adjusted analyses led to consistent results. For continuous outcomes, the DSMD was 0.13 (0.06 to 0.19). It was lower for MAs with a pharmacological intervention [-0.03, (-0.12 to 0.07)], an objective outcome [0.05, (-0.08 to 0.17)] or after adjusting for trial size [0.06, (-0.01 to 0.15)].

Conclusion

For binary outcomes, CRTs and IRTs can safely be pooled in MAs because of an absence of systematic differences between effect estimates. For continuous outcomes, the results were less clear although accounting for trial sample sizes led to a non-significant difference. More research is needed for continuous outcomes and, meanwhile, MAs should be completed with subgroup analyses (CRTs vs IRTs).

SUBMITTER: Leyrat C

PROVIDER: S-EPMC6469309 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Intervention effect estimates in cluster randomized versus individually randomized trials: a meta-epidemiological study.

Leyrat Clémence C Caille Agnès A Eldridge Sandra S Kerry Sally S Dechartres Agnès A Giraudeau Bruno B

International journal of epidemiology 20190401 2

<h4>Background</h4>Cluster randomized trials (CRTs) and individually randomized trials (IRTs) are often pooled together in meta-analyses (MAs) of randomized trials. However, the potential systematic differences in intervention effect estimates between these two trial types has never been investigated. Therefore, we conducted a meta-epidemiological study comparing intervention effect estimates between CRTs and IRTs.<h4>Methods</h4>All Cochrane MAs including at least one CRT and one IRT, published ...[more]

PMID: 30418549

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Project description:BackgroundThe randomized controlled trial (RCT) is considered the gold standard study design to inform decisions about the effectiveness of interventions. However, a common limitation is inadequate reporting of the applicability of the intervention and trial results for people who are "socially disadvantaged" and this can affect policy-makers' decisions. We previously developed a framework for identifying health-equity-relevant trials, along with a reporting guideline for transparent reporting. In this study, we provide a descriptive assessment of health-equity considerations in 200 randomly sampled equity-relevant trials.MethodsWe developed a search strategy to identify health-equity-relevant trials published between 2013 and 2015. We randomly sorted the 4316 records identified by the search and screened studies until 100 individually randomized (RCTs) and 100 cluster randomized controlled trials (CRTs) were identified. We developed and pilot-tested a data extraction form based on our initial work, to inform the development of our reporting guideline for equity-relevant randomized trials.ResultsIn total, 39 trials (20%) were conducted in a low- and middle-income country and 157 trials (79%) in a high-income country focused on socially disadvantaged populations (78% CRTs, 79% RCTs). Seventy-four trials (37%) reported a subgroup analysis across a population characteristic associated with disadvantage (25% CRT, 49% RCTs), with 19% of included studies reporting subgroup analyses across sex, 9% across race/ethnicity/culture, and 4% across socioeconomic status. No subgroup analyses were reported for place of residence, occupation, religion, education, or social capital. One hundred and forty-one trials (71%) discussed the applicability of their results to one or more socially disadvantaged populations (68% of CRT, 73% of RCT).DiscussionIn this set of trials, selected for their relevance to health equity, data that were disaggregated for socially disadvantaged populations were rarely reported. We found that even when the data are available, opportunities to analyze health-equity considerations are frequently missed. The recently published equity extension of the Consolidated Reporting Standards for Randomized Trials (CONSORT-Equity) may help improve delineation of hypotheses related to socially disadvantaged populations, and transparency and completeness of reporting of health-equity considerations in RCTs. This study can serve as a baseline assessment of the reporting of equity considerations.

Project description:ObjectiveTo examine whether deviation from the standard intention to treat analysis has an influence on treatment effect estimates of randomised trials.DesignMeta-epidemiological study.Data sourcesMedline, via PubMed, searched between 2006 and 2010; 43 systematic reviews of interventions and 310 randomised trials were included.Eligibility criteria for selecting studiesFrom each year searched, random selection of 5% of intervention reviews with a meta-analysis that included at least one trial that deviated from the standard intention to treat approach. Basic characteristics of the systematic reviews and randomised trials were extracted. Information on the reporting of intention to treat analysis, outcome data, risk of bias items, post-randomisation exclusions, and funding were extracted from each trial. Trials were classified as: ITT (reporting the standard intention to treat approach), mITT (reporting a deviation from the standard approach), and no ITT (reporting no approach). Within each meta-analysis, treatment effects were compared between mITT and ITT trials, and between mITT and no ITT trials. The ratio of odds ratios was calculated (value <1 indicated larger treatment effects in mITT trials than in other trial categories).Results50 meta-analyses and 322 comparisons of randomised trials (from 84 ITT trials, 118 mITT trials, and 108 no ITT trials; 12 trials contributed twice to the analysis) were examined. Compared with ITT trials, mITT trials showed a larger intervention effect (pooled ratio of odds ratios 0.83 (95% confidence interval 0.71 to 0.96), P=0.01; between meta-analyses variance τ(2)=0.13). Adjustments for sample size, type of centre, funding, items of risk of bias, post-randomisation exclusions, and variance of log odds ratio yielded consistent results (0.80 (0.69 to 0.94), P=0.005; τ(2)=0.08). After exclusion of five influential studies, results remained consistent (0.85 (0.75 to 0.98); τ(2)=0.08). The comparison between mITT trials and no ITT trials showed no statistical difference between the two groups (adjusted ratio of odds ratios 0.92 (0.70 to 1.23); τ(2)=0.57).ConclusionsTrials that deviated from the intention to treat analysis showed larger intervention effects than trials that reported the standard approach. Where an intention to treat analysis is impossible to perform, authors should clearly report who is included in the analysis and attempt to perform multiple imputations.

Project description:ObjectiveTo compare estimates of intervention effects between single centre and multicentre randomised controlled trials with continuous outcomes.DesignMeta-epidemiological study.Data sources26 meta-analyses totalling 292 randomised controlled trials (177 single centre, 115 multicentre) with continuous outcomes published between January 2007 and January 2010 in the Cochrane database of systematic reviews.Data extractionData were extracted on characteristics of trials, single or multicentre status, risk of bias using the risk of bias tool of the Cochrane Collaboration, and results.Data synthesisThe intervention effects were estimated with standardised mean differences. For each meta-analysis, random effects meta-regression was used to estimate the difference in standardised mean differences between single centre and multicentre trials. Differences in standardised mean differences were then pooled across meta-analyses by a random-effects meta-analysis model. A combined difference in standardised mean differences of less than 0 indicated that single centre trials showed larger treatment effects, on average, than did multicentre trials. Because single centre trials may be more prone to publication bias and may have lower methodological quality than multicentre trials, sensitivity analyses were done with adjustment for sample size and domains of the risk of bias tool.ResultsSingle centre trials showed larger intervention effects than did multicentre trials (combined difference in standardised mean differences -0.09, 95% confidence interval -0.17 to -0.01, P=0.04), with low heterogeneity across individual meta-analyses (I(2)=0%, between meta-analyses variance τ(2)=0.00). Adjustment for sample size slightly attenuated the difference (-0.08, -0.17 to 0.01). Adjustment for risk of bias yielded similar estimates with wider confidence intervals, some of them crossing 0 (-0.09, -0.17 to 0.00 for overall risk of bias).ConclusionsOn average, single centre clinical trials with continuous outcomes showed slightly larger intervention effects than did multicentre trials. Further research is needed to investigate potential causes of these differences.

Project description:ObjectiveTo assess the influence of trial sample size on treatment effect estimates within meta-analyses.DesignMeta-epidemiological study.Data sources93 meta-analyses (735 randomised controlled trials) assessing therapeutic interventions with binary outcomes, published in the 10 leading journals of each medical subject category of the Journal Citation Reports or in the Cochrane Database of Systematic Reviews.Data extractionSample size, outcome data, and risk of bias extracted from each trial.Data synthesisTrials within each meta-analysis were sorted by their sample size: using quarters within each meta-analysis (from quarter 1 with 25% of the smallest trials, to quarter 4 with 25% of the largest trials), and using size groups across meta-analyses (ranging from <50 to ≥ 1000 patients). Treatment effects were compared within each meta-analysis between quarters or between size groups by average ratios of odds ratios (where a ratio of odds ratios less than 1 indicates larger effects in smaller trials).ResultsTreatment effect estimates were significantly larger in smaller trials, regardless of sample size. Compared with quarter 4 (which included the largest trials), treatment effects were, on average, 32% larger in trials in quarter 1 (which included the smallest trials; ratio of odds ratios 0.68, 95% confidence interval 0.57 to 0.82), 17% larger in trials in quarter 2 (0.83, 0.75 to 0.91), and 12% larger in trials in quarter 3 (0.88, 0.82 to 0.95). Similar results were obtained when comparing treatment effect estimates between different size groups. Compared with trials of 1000 patients or more, treatment effects were, on average, 48% larger in trials with fewer than 50 patients (0.52, 0.41 to 0.66) and 10% larger in trials with 500-999 patients (0.90, 0.82 to 1.00).ConclusionsTreatment effect estimates differed within meta-analyses solely based on trial sample size, with stronger effect estimates seen in small to moderately sized trials than in the largest trials.

Project description:Percutaneous coronary intervention with bivalirudin plus bail-out glycoprotein IIb/IIIa inhibitors has been shown to be as effective as unfractionated heparin plus routine glycoprotein IIb/IIIa inhibitors in preventing cardiac ischemic events, but with a lower bleeding risk. It is unknown whether bivalirudin would have the same beneficial effects if compared with heparin when the use of glycoprotein IIb/IIIa inhibitors was similar between treatment arms. We searched the MEDLINE, Web of Science, and Cochrane databases from inception until March 2015 for randomized trials that compared bivalirudin to heparin in patients undergoing percutaneous coronary intervention. We required that the intended use of glycoprotein IIb/IIIa inhibitors was similar between the study groups. Summary estimates were principally constructed by the Peto method. Fifteen trials met our inclusion criteria, which yielded 25,824 patients. Bivalirudin versus heparin was associated with an increased hazard of stent thrombosis (odds ratio [OR] 1.49, 95% confidence interval [CI] 1.15-1.92, P = .002, I2 = 16.9%), with a similar hazard of myocardial infarction (OR 1.09, 95% CI 0.98-1.22, P = .11, I2 = 35.8%), all-cause mortality (OR 0.88, 95% CI 0.72-1.08, P = .21, I2 = 31.5%) and major adverse cardiac events (OR 1.04, 95% CI 0.94-1.14, P = .46, I2 = 53.9%). Bivalirudin was associated with a reduced hazard of major bleeding (OR 0.80, 95% CI 0.70-0.92, P = .001, I2 = 63.5%). The dose of heparin in the control arm modified this association; when the dose of unfractionated heparin in the control arm was ≥ 100 units/kg, bivalirudin was associated with a reduction in major bleeding (OR 0.55, 95% CI 0.45-0.68, P < .0001), but when the dose of unfractionated heparin was ≤ 75 units/kg, bivalirudin was not associated with reduction in bleeding (OR 1.09, 95% CI 0.91-1.31, P = .36). Among patients undergoing PCI, bivalirudin was associated with an increased hazard of stent thrombosis. Bivalirudin may be associated with a reduced hazard of major bleeding; however, this benefit was no longer apparent when compared with a dose of unfractionated heparin ≤ 75 units/kg.

Dataset Information

Intervention effect estimates in cluster randomized versus individually randomized trials: a meta-epidemiological study.

Background

Methods

Results

Conclusion

Publications

Intervention effect estimates in cluster randomized versus individually randomized trials: a meta-epidemiological study.

Similar Datasets

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