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Optimal design of longitudinal data analysis using generalized estimating equation models.

Jingxia Liu1, Graham A Colditz2

  • 1Division of Public Health Sciences, Department of Surgery, Washington University in Saint Louis (WUSTL), St Louis, MO, 63110, USA.

Biometrical Journal. Biometrische Zeitschrift
|November 24, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for calculating optimal sample sizes in longitudinal studies using generalized estimating equations (GEE). It maximizes statistical power within a fixed budget, even when correlation parameters are unknown.

Keywords:
Generalized estimating equationLongitudinal studiesOptimal designPowerWorking correlation matrix

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Clinical Trial Design

Background:

  • Longitudinal studies are crucial in biomedical research for evaluating treatment effects.
  • Accurate sample size calculation and analysis must account for within-subject correlation.
  • Generalized Estimating Equations (GEE) are a key method for analyzing such data, incorporating working correlation structures.

Purpose of the Study:

  • To propose optimal sample size and repeated measurements calculations for longitudinal studies using GEE.
  • To maximize statistical power under a fixed budget, considering an exchangeable working correlation matrix.
  • To develop methods for scenarios with known and unknown association parameters (ρ).

Main Methods:

  • Utilized Generalized Estimating Equations (GEE) with an exchangeable working correlation matrix.
  • Derived equations for sample size and number of repeated measurements based on a fixed budget.
  • Developed an algorithm for estimating optimal parameters when ρ is unknown.

Main Results:

  • Provided explicit formulas for sample size and repeated measurements for known ρ.
  • Developed a practical algorithm for unknown ρ, enhancing flexibility in study design.
  • Demonstrated applicability to scenarios with different true and working correlation matrices.

Conclusions:

  • The proposed GEE-based method offers optimal sample size and measurement frequency for fixed budgets.
  • The approach is robust, handling unknown correlation parameters and different correlation structures.
  • This contributes to more efficient and powerful longitudinal study designs in biomedical research.