Dataset Information

Very large treatment effects in randomised trials as an empirical marker to indicate whether subsequent trials are necessary: meta-epidemiological assessment.

ABSTRACT:

Objective

To examine whether a very large effect (VLE; defined as a relative risk of ?0.2 or ?5) in a randomised trial could be an empirical marker that subsequent trials are unnecessary.

Design

Meta-epidemiological assessment of existing published data on randomised trials.

Data sources

Cochrane Database of Systematic Reviews (2010, issue 7) with data on subsequent large trials updated to 2015, issue 12.

Eligibility criteria

All binary outcome forest plots were selected, which contained an index randomised trial with a VLE that was nominally statistically significant (P<0.05), included a subsequent large randomised trial (?200 events and ?200 non-events) for validation of the effect, assessed a primary outcome of the review, and was not a subgroup or sensitivity analysis.

Results

Of 3082 reviews yielding 85?002 forest plots, only 44 (0.05%) satisfied the inclusion criteria. Index trials were generally small, with a median sample of 99 (median 14 events). Few index trials were rated at low risk of bias (9 of 44; 20%). The relative risk was closer to the null in the subsequent large trials in 43 of 44 cases. Subsequent large trial data failed to find a statistically significant (P<0.05) effect in the same direction in 19 cases (43%, 95% confidence interval 29% to 58%). Even when the subsequent large trials did find a significant effect in the same direction, the additional primary outcomes in most of these trials would have to be considered before deciding in favour of using the intervention. Subsequent large trial data found a statistically significant effect in the same direction in 19 of 21 cases when the index trial also had a value of P<0.001.

Conclusions

The frequency of VLEs followed by a large trial is vanishingly small, and where they occur they do not appear to be a reliable marker for a benefit that is reproducible and directly actionable. An empirical rule using a VLE in a randomised controlled trial as a marker that further trials are unnecessary would be neither practical nor useful. Caution should be taken when interpreting small studies with very large treatment effects.

SUBMITTER: Nagendran M

PROVIDER: S-EPMC5081692 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Very large treatment effects in randomised trials as an empirical marker to indicate whether subsequent trials are necessary: meta-epidemiological assessment.

Nagendran Myura M Pereira Tiago V TV Kiew Grace G Altman Douglas G DG Maruthappu Mahiben M Ioannidis John P A JP McCulloch Peter P

BMJ (Clinical research ed.) 20161027

<h4>Objective</h4> To examine whether a very large effect (VLE; defined as a relative risk of ≤0.2 or ≥5) in a randomised trial could be an empirical marker that subsequent trials are unnecessary.<h4>Design</h4> Meta-epidemiological assessment of existing published data on randomised trials.<h4>Data sources</h4> Cochrane Database of Systematic Reviews (2010, issue 7) with data on subsequent large trials updated to 2015, issue 12.<h4>Eligibility criteria</h4> All binary outcome forest plots were ...[more]

PMID: 27789483

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Project description:ObjectiveTo assess differences in estimated treatment effects for mortality between observational studies with routinely collected health data (RCD; that are published before trials are available) and subsequent evidence from randomized controlled trials on the same clinical question.DesignMeta-epidemiological survey.Data sourcesPubMed searched up to November 2014.MethodsEligible RCD studies were published up to 2010 that used propensity scores to address confounding bias and reported comparative effects of interventions for mortality. The analysis included only RCD studies conducted before any trial was published on the same topic. The direction of treatment effects, confidence intervals, and effect sizes (odds ratios) were compared between RCD studies and randomized controlled trials. The relative odds ratio (that is, the summary odds ratio of trial(s) divided by the RCD study estimate) and the summary relative odds ratio were calculated across all pairs of RCD studies and trials. A summary relative odds ratio greater than one indicates that RCD studies gave more favorable mortality results.ResultsThe evaluation included 16 eligible RCD studies, and 36 subsequent published randomized controlled trials investigating the same clinical questions (with 17,275 patients and 835 deaths). Trials were published a median of three years after the corresponding RCD study. For five (31%) of the 16 clinical questions, the direction of treatment effects differed between RCD studies and trials. Confidence intervals in nine (56%) RCD studies did not include the RCT effect estimate. Overall, RCD studies showed significantly more favorable mortality estimates by 31% than subsequent trials (summary relative odds ratio 1.31 (95% confidence interval 1.03 to 1.65; I(2)=0%)).ConclusionsStudies of routinely collected health data could give different answers from subsequent randomized controlled trials on the same clinical questions, and may substantially overestimate treatment effects. Caution is needed to prevent misguided clinical decision making.

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Project description:IntroductionAlthough interest in including non-randomised studies of interventions (NRSIs) in meta-analysis of randomised controlled trials (RCTs) is growing, estimates of effectiveness obtained from NRSIs are vulnerable to greater bias than RCTs. The objectives of this study are to: (1) explore how NRSIs can be integrated into a meta-analysis of RCTs; (2) assess concordance of the evidence from non-randomised and randomised trials and explore factors associated with agreement; and (3) investigate the impact on estimates of pooled bodies of evidence when NRSIs are included.Methods and analysisWe will conduct a systematic survey of 210 systematic reviews that include both RCTs and NRSIs, published from 2017 to 2022. We will randomly select reviews, stratified in a 1:1 ratio by Core vs non-Core clinical journals, as defined by the National Library of Medicine. Teams of paired reviewers will independently determine eligibility and abstract data using standardised, pilot-tested forms. The concordance of the evidence will be assessed by exploring agreement in the relative effect reported by NRSIs and RCT addressing the same clinical question, defined as similarity of the population, intervention/exposure, control and outcomes. We will conduct univariable and multivariable logistic regression analyses to examine the association of prespecified study characteristics with agreement in the estimates between NRSIs and RCTs. We will calculate the ratio of the relative effect estimate from NRSIs over that from RCTs, along with the corresponding 95% CI. We will use a bias-corrected meta-analysis model to investigate the influence on pooled estimates when NRSIs are included in the evidence synthesis.Ethics and disseminationEthics approval is not required. The findings of this study will be disseminated through peer-reviewed publications, conference presentations and condensed summaries for clinicians, health policymakers and guideline developers regarding the design, conduct, analysis, and interpretation of meta-analysis that integrate RCTs and NRSIs.

Dataset Information

Very large treatment effects in randomised trials as an empirical marker to indicate whether subsequent trials are necessary: meta-epidemiological assessment.

Objective

Design

Data sources

Eligibility criteria

Results

Conclusions

Publications

Very large treatment effects in randomised trials as an empirical marker to indicate whether subsequent trials are necessary: meta-epidemiological assessment.

Similar Datasets

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