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Related Experiment Videos

Sample size for a two-group comparison of repeated binary measurements using GEE.

Sin-Ho Jung1, Chul W Ahn

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA. jung0005@mc.duke.edu

Statistics in Medicine
|August 25, 2005
PubMed
Summary

This study introduces a new sample size formula for comparing binary repeated measurements in clinical trials using generalized estimating equations (GEE). The formula accounts for missing data and correlation structures, improving trial design for longitudinal binary outcomes.

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Longitudinal Data Analysis

Background:

  • Controlled clinical trials frequently involve repeated measurements over time.
  • Comparing change rates between treatment groups is a common objective.
  • Repeated measurements often present challenges with missing data and serial correlation.

Purpose of the Study:

  • To propose a closed-form sample size formula for comparing change rates of binary repeated measurements using generalized estimating equations (GEE).
  • To enhance sample size calculations for two-group comparisons in longitudinal studies with binary outcomes.
  • To provide a practical tool for researchers designing clinical trials with repeated binary measures.

Main Methods:

  • Derivation of a closed-form sample size formula based on GEE.

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  • Incorporation of various missing data patterns (independent, monotone).
  • Inclusion of correlation structures like the AR(1) model.
  • Development of an algorithm for generating correlated binary data with Markov dependency for simulations.

    • Main Results:

    • A novel sample size formula is presented for GEE-based analysis of binary longitudinal data.
    • The formula explicitly considers the impact of missing data and correlation structures.
    • Simulation studies validate the proposed methods and data generation algorithm.

    Conclusions:

    • The proposed sample size formula offers a more accurate approach for planning clinical trials with binary repeated measurements.
    • Accounting for missing data and correlation improves the efficiency and power of such trials.
    • The developed data generation algorithm aids in simulating realistic longitudinal binary datasets for methodological research.