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Sample size calculations with multiplicity adjustment for longitudinal clinical trials with missing data.
1Department of Biostatistics, Forest Laboratories, Harborside Financial Center Plaza V, Jersey City, NJ 07311, USA. Kaifeng.Lu@frx.com
Statistics in Medicine
|December 14, 2011
Summary
This study simplifies sample size calculations for longitudinal clinical trials with missing data. The method converts longitudinal data to cross-sectional data, reducing simulation complexity and improving power evaluations.
Area of Science:
- Clinical Trials
- Biostatistics
- Longitudinal Data Analysis
Background:
- Missing data are common in longitudinal clinical trials, affecting statistical power.
- Multiplicity adjustment is crucial for sample size calculations with multiple doses and endpoints.
Purpose of the Study:
- To present a simplified method for sample size calculations in longitudinal clinical trials with missing data.
- To address multiplicity adjustment alongside missing data considerations.
Main Methods:
- Converting longitudinal data with missing values into cross-sectional data without missing values.
- Utilizing this conversion to simplify power and sample size evaluations.
Main Results:
- The proposed approach significantly simplifies simulation procedures.
- Facilitates robust power evaluation across diverse clinical trial scenarios.
Conclusions:
- This method offers a more efficient approach to sample size calculations for complex longitudinal trials.
- Aids researchers in accurately determining sample sizes while accounting for missing data and multiplicity.
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