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Sample Size Estimation for Correlated Count Data With Changes in Dispersion.

Jintong Hou1, Leslie A McClure2, Savina Jaeger3

  • 1Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA.

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|February 5, 2025
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Summary
This summary is machine-generated.

This study provides new formulas for sample size and power calculations in clinical trials with repeated count measurements. These methods account for changes in data dispersion and correlations, improving accuracy for various study designs.

Keywords:
GEEcorrelated count measurementnegative binomial distributionnon‐inferioritysample sizezero‐inflated distribution

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Longitudinal Data Analysis

Background:

  • Clinical studies often involve repeated measurements, requiring specialized sample size and power calculations.
  • Count data in clinical trials (e.g., bleeding events) can exhibit changing dispersion and correlation over time.
  • Generalized Estimating Equations (GEE) is a common method for analyzing correlated count data.

Purpose of the Study:

  • To investigate the performance of GEE for count outcomes with changing dispersion.
  • To derive accurate sample size and power calculation formulas for correlated count data under GEE.
  • To propose modified methods for negative binomial distributions to address Type I error inflation.

Main Methods:

  • Derived closed-form formulas for sample size and power estimation using GEE.
  • Formulas accommodate varying dispersion parameters and distributions (Poisson, negative binomial, zero-inflated variants) across paired measurements.
  • Conducted simulations to evaluate empirical power and Type I error rates.

Main Results:

  • Developed general formulas for sample size and power applicable to intra-participant comparisons, RCTs, and matched pairs.
  • Formulas are robust even when pre- and post-intervention measurements follow different distributions.
  • Modified methods demonstrated effectiveness in controlling Type I error for negative binomial distributions with significant dispersion changes.

Conclusions:

  • The derived GEE-based formulas provide accurate sample size and power estimations for longitudinal count data with changing dispersion.
  • The proposed modifications enhance reliability for negative binomial models, crucial in many clinical settings.
  • The study offers practical tools (R functions) for researchers designing clinical trials with complex correlated count outcomes.