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Stopping rules and estimation problems in clinical trials.

M D Hughes1, S J Pocock

  • 1Department of Clinical Epidemiology and General Practice, Royal Free Hospital School of Medicine, London, U.K.

Statistics in Medicine
|December 1, 1988
PubMed
Summary
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Clinical trial stopping rules can inflate treatment effect estimates, especially when trials end early or use continuous monitoring. A Bayesian approach helps provide more accurate treatment effect ranges, particularly for early-stopping trials.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Medical Research Methodology

Background:

  • Stopping rules in clinical trials are used to terminate studies early for efficacy or futility.
  • These rules can introduce bias in estimating the true magnitude of treatment effects.

Purpose of the Study:

  • To investigate the bias in treatment effect estimation caused by stopping rules in clinical trials.
  • To propose a Bayesian method for more accurate estimation of treatment effects, especially in early-stopping trials.

Main Methods:

  • Simulation of a post-myocardial infarction trial estimating risk ratios.
  • Evaluation of bias under various group sequential plans and continuous naive monitoring.
  • Development and application of a Bayesian approach using simulated trial populations.

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Main Results:

  • Group sequential designs show small median bias for fixed effects, but bias increases with detectable effects.
  • Bias is more pronounced in early-stopping trials and dramatic under naive monitoring.
  • Group sequential plans create multimodal sampling distributions, complicating meta-analyses.

Conclusions:

  • Stopping rules can significantly bias treatment effect estimates, particularly in early-stopping trials.
  • A proposed Bayesian method offers a way to assess plausible true treatment effects using interim results.
  • This Bayesian approach aids in shrinking biased estimates and has implications for future clinical trial design.