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Effects of correlation and missing data on sample size estimation in longitudinal clinical trials
1Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Pharmaceutical Statistics
|December 5, 2008
Summary
This study evaluates the Generalized Estimating Equation (GEE) sample size formula for longitudinal trials. Findings assess its accuracy with small sample sizes and non-random missing data, crucial for clinical trial design.
Area of Science:
- Biostatistics
- Clinical Trials
- Longitudinal Data Analysis
Background:
- Comparing rates of change in longitudinal clinical trials is a common objective.
- Generalized Estimating Equations (GEE) are robust for analyzing such data, handling correlation misspecification and missing data.
Purpose of the Study:
- To investigate the performance of the Generalized Estimating Equation (GEE) sample size formula.
- To assess the formula's accuracy under conditions of small sample sizes, specific correlation structures (damped exponential), and non-ignorable missing data.
Main Methods:
- A simulation study was conducted.
- The study examined the GEE sample size formula's performance using various scenarios.
Main Results:
- The simulation results provide insights into the GEE sample size formula's behavior under challenging conditions.
- Performance metrics were evaluated for small sample sizes and non-random missing data.
Conclusions:
- The findings offer guidance on the appropriate application of the GEE sample size formula in longitudinal clinical trials.
- Understanding the formula's limitations is essential for accurate study design and power calculations.
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