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Sample size for repeated measures studies with binary responses

S R Lipsitz1, G M Fitzmaurice

  • 1Department of Biostatistics, Harvard School of Public Health, Boston MA 02115.

Statistics in Medicine
|June 30, 1994
PubMed
Summary
This summary is machine-generated.

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Calculating the minimum sample size for binary repeated measures studies is crucial. This study proposes weighted least squares (WLS) to determine sample size for detecting clinically important treatment effects in binary outcomes.

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Statistical Methods

Background:

  • Sample size determination is critical for the validity and power of clinical trials.
  • Repeated measures designs are common in clinical research, particularly when dealing with binary outcomes.
  • Accurate sample size calculations ensure efficient resource allocation and ethical study conduct.

Purpose of the Study:

  • To propose a method for calculating the minimum sample size for repeated measures studies with binary response variables.
  • To provide guidance on determining sample size when aiming to detect a minimum clinically important treatment effect.
  • To discuss practical considerations for sample size determination in the context of binary repeated measures data.

Main Methods:

  • The study proposes the use of weighted least squares (WLS) for sample size calculation.

Related Experiment Videos

  • The WLS method is applied to determine the minimum sample size needed to detect a specified treatment effect.
  • Tabulated values for estimated sample sizes are provided for a illustrative example.

    • Main Results:

    • The weighted least squares (WLS) method offers a viable approach for sample size estimation in binary repeated measures studies.
    • The study provides concrete examples and tabulated data to aid researchers in sample size determination.
    • The findings highlight the importance of considering the specific characteristics of binary repeated measures data when planning studies.

    Conclusions:

    • Weighted least squares (WLS) is a recommended statistical method for sample size calculations in studies with repeated binary outcomes.
    • Researchers should carefully consider practical aspects when determining sample size for such study designs.
    • The proposed methodology facilitates robust study planning and enhances the likelihood of detecting meaningful clinical treatment effects.