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Related Experiment Videos

Sample size for comparing linear growth curves.

H I Patel1, E Rowe

  • 1Clinical and Scientific Affairs, Central Research Division Pfizer, Inc., New York, New York 10017-5755, USA.

Journal of Biopharmaceutical Statistics
|June 24, 1999
PubMed
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This study provides a method for calculating sample size in clinical trials with repeated measurements. It ensures sufficient power for detecting treatment differences in exponential family data.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Longitudinal Data Analysis

Background:

  • Comparing treatment efficacy often involves repeated measurements over time.
  • Accurate sample size calculation is crucial for the validity and efficiency of clinical trials.
  • Longitudinal data analysis requires specialized statistical methods to account for within-subject correlations.

Purpose of the Study:

  • To develop a sample size computation method for comparing two treatment groups in longitudinal studies.
  • To address scenarios where repeated measurements follow exponential family distributions.
  • To provide a framework for sample size determination under a linear growth curve model.

Main Methods:

  • Utilized a linear growth curve model with a suitable link function.

Related Experiment Videos

  • Employed generalized estimating equations (GEE) to estimate regression parameters.
  • Derived sample size based on the asymptotic normality of the difference between treatment slopes (delta).
  • Incorporated dispersion matrices under null and alternative hypotheses using a working correlation matrix.

    • Main Results:

    • A formula for sample size calculation was derived for comparing two treatment groups.
    • The method is applicable to repeated measures designs with binary and count data.
    • Numerical examples illustrate the practical application of the sample size computation.

    Conclusions:

    • The proposed method offers a statistically sound approach to sample size determination in longitudinal studies.
    • This methodology facilitates robust clinical trial design for interventions with repeated outcome measures.
    • The findings are particularly relevant for trials involving binary or count data analyzed using generalized linear models.