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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Sample size calculation for cluster randomization trials with a time-to-event endpoint.

Jianghao Li1, Sin-Ho Jung1

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

Statistics in Medicine
|January 19, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new sample size formula for cluster randomization trials with time-to-event outcomes. The formula aids researchers in planning studies comparing survival distributions between groups.

Keywords:
censoringinflation factorintracluster correlation coefficientvariable cluster sizeweighted rank test

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Cluster randomization trials (CRTs) involve randomizing groups of subjects to intervention arms.
  • Observations within clusters are often correlated due to shared characteristics.
  • Time-to-event outcomes with censoring are common in CRTs.

Purpose of the Study:

  • To propose a closed-form sample size formula for weighted rank tests in CRTs.
  • To facilitate sample size calculations for comparing survival distributions under cluster randomization.
  • To address variable cluster sizes in the sample size estimation.

Main Methods:

  • Development of a closed-form sample size formula.
  • Utilizing weighted rank tests for marginal survival distribution comparisons.
  • Application to cluster randomization trials with potentially varying cluster sizes.

Main Results:

  • The proposed sample size formula is presented.
  • Simulation studies demonstrate the formula's performance across various settings.
  • The method is illustrated using real-world study examples.

Conclusions:

  • The developed formula provides a practical tool for sample size determination in CRTs.
  • The findings support accurate planning of cluster randomized trials with survival endpoints.
  • The method is robust and applicable to diverse study designs.