Stopping randomized trials early for benefit: a protocol of the Study Of Trial Policy Of Interim Truncation-2 STOPIT-2Report as inadecuate

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, 10:49

First Online: 06 July 2009Received: 11 February 2009Accepted: 06 July 2009DOI: 10.1186-1745-6215-10-49

Cite this article as: Briel, M., Lane, M., Montori, V.M. et al. Trials 2009 10: 49. doi:10.1186-1745-6215-10-49


BackgroundRandomized clinical trials RCTs stopped early for benefit often receive great attention and affect clinical practice, but pose interpretational challenges for clinicians, researchers, and policy makers. Because the decision to stop the trial may arise from catching the treatment effect at a random high, truncated RCTs tRCTs may overestimate the true treatment effect. The St udy O f Trial P olicy Of I nterim T runcation STOPIT-1, which systematically reviewed the epidemiology and reporting quality of tRCTs, found that such trials are becoming more common, but that reporting of stopping rules and decisions were often deficient. Most importantly, treatment effects were often implausibly large and inversely related to the number of the events accrued. The aim of STOPIT-2 is to determine the magnitude and determinants of possible bias introduced by stopping RCTs early for benefit.

Methods-DesignWe will use sensitive strategies to search for systematic reviews addressing the same clinical question as each of the tRCTs identified in STOPIT-1 and in a subsequent literature search. We will check all RCTs included in each systematic review to determine their similarity to the index tRCT in terms of participants, interventions, and outcome definition, and conduct new meta-analyses addressing the outcome that led to early termination of the tRCT. For each pair of tRCT and systematic review of corresponding non-tRCTs we will estimate the ratio of relative risks, and hence estimate the degree of bias. We will use hierarchical multivariable regression to determine the factors associated with the magnitude of this ratio. Factors explored will include the presence and quality of a stopping rule, the methodological quality of the trials, and the number of total events that had occurred at the time of truncation.

Finally, we will evaluate whether Bayesian methods using conservative informative priors to -regress to the mean- overoptimistic tRCTs can correct observed biases.

DiscussionA better understanding of the extent to which tRCTs exaggerate treatment effects and of the factors associated with the magnitude of this bias can optimize trial design and data monitoring charters, and may aid in the interpretation of the results from trials stopped early for benefit.

AbbreviationsRCTsRandomized clinical trials

tRCTsTruncated randomized clinical trials due to stopping early for benefit

STOPITStudy of Trial Policy of Interim Truncation

MESHMedical Subject Heading

logRRLogarithm of the relative risk

CONSORTConsolidated Standards of Reporting Trials

QUOROMQuality of Reporting of Meta-analyses

PICOPatient population, intervention, control, outcome.

Electronic supplementary materialThe online version of this article doi:10.1186-1745-6215-10-49 contains supplementary material, which is available to authorized users.

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Author: Matthias Briel - Melanie Lane - Victor M Montori - Dirk Bassler - Paul Glasziou - German Malaga - Elie A Akl - Ignacio F


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