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Group sequential testing for cluster randomized trials with time-to-event endpoint.

Jianghao Li1, Sin-Ho Jung1

  • 1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina.

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|June 2, 2021
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Summary
This summary is machine-generated.

This study introduces group sequential methods for cluster randomized trials with time-to-event outcomes. The proposed methods enable efficient sequential monitoring of cluster randomized trials (CRTs) using survival data.

Keywords:
alpha spending functiondependent incrementexpected sample sizeintracluster correlation coefficientlog-rank testvariable cluster size

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Survival Analysis

Background:

  • Cluster randomized trials (CRTs) are increasingly used in health research.
  • Sequential monitoring of CRTs with time-to-event endpoints presents unique statistical challenges.
  • Existing methods for sequential analysis may not be directly applicable to clustered survival data.

Purpose of the Study:

  • To develop and evaluate group sequential methods for CRTs with time-to-event endpoints.
  • To address the statistical complexities of sequential monitoring in clustered survival data.
  • To provide a framework for sample size determination in such trials.

Main Methods:

  • Utilizing the alpha spending function approach for interim data analysis.
  • Deriving the joint distribution of test statistics and information fractions for CRTs.
  • Proving that sequentially computed log-rank statistics in CRTs lack the independent increment property.
  • Proposing an information fraction tailored for group sequential trials with clustered survival data.
  • Developing a corresponding sample size determination approach.

Main Results:

  • The proposed group sequential testing procedure is evaluated through extensive simulation studies.
  • Performance is assessed based on expected and maximal sample sizes using various alpha spending functions.
  • Demonstrated the non-independent increment property of sequentially computed log-rank statistics in CRTs.
  • The proposed information fraction and sample size determination methods are validated.

Conclusions:

  • The developed group sequential methods provide a robust framework for monitoring CRTs with time-to-event outcomes.
  • The findings offer practical tools for efficient trial design and sample size calculation in clustered survival data settings.
  • Real study examples illustrate the applicability and utility of the proposed methodology.