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Related Experiment Videos

Sample size calculation for weighted rank tests comparing survival distributions under cluster randomization: a

Sin-Ho Jung1

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705, USA. sinho.jung@duke.edu

Journal of Biopharmaceutical Statistics
|September 22, 2007
PubMed
Summary
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We developed a new method to calculate the necessary number of clusters for survival studies using cluster randomization. This approach accounts for variable cluster sizes and ensures accurate sample size determination for rank tests.

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Survival Analysis

Background:

  • Cluster randomization is frequently used in comparative effectiveness research.
  • Calculating sample size for cluster-randomized trials with survival outcomes and variable cluster sizes presents unique challenges.
  • Existing methods may not adequately address the complexities of intracluster correlation and varying group sizes in survival data.

Purpose of the Study:

  • To propose a novel sample size calculation method for rank tests comparing two survival distributions.
  • To specifically address cluster randomization designs with potentially variable cluster sizes.
  • To provide a robust method for determining the required number of clusters in such studies.

Main Methods:

  • The proposed method utilizes a simulation procedure.

Related Experiment Videos

  • It generates clustered exponential survival variables based on specified marginal hazard rates and intracluster correlation coefficients.
  • Sample size is calculated considering significance level, power, hazard rates, intracluster correlation, accrual rate, follow-up period, and cluster size distribution.

    • Main Results:

    • A simulation-based method for sample size calculation in cluster-randomized survival studies has been developed.
    • The method accounts for variable cluster sizes and intracluster correlation.
    • It provides a practical tool for researchers planning such trials.

    Conclusions:

    • The proposed method offers a statistically sound approach for determining sample size in complex survival studies.
    • It is applicable to rank tests under cluster randomization with variable cluster sizes.
    • This facilitates more efficient and accurate planning of clinical trials involving survival endpoints.