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Closed-form REML estimators and sample size determination for mixed effects models for repeated measures under
1Shire, 300 Shire Way, Lexington, 02421, MA, U.S.A.
Statistics in Medicine
|February 23, 2017
Summary
This study presents new statistical methods for analyzing repeated measures data with missing values. The findings offer a way to calculate statistical power and sample size for clinical trials using mixed models for repeated measures (MMRM).
Area of Science:
- Statistics
- Biostatistics
- Clinical Trial Design
Background:
- Missing data in longitudinal studies complicate analysis.
- Mixed Effects Models for Repeated Measures (MMRM) are widely used.
- Accurate power and sample size calculations are crucial for clinical trial efficiency.
Purpose of the Study:
- To derive estimators for fixed effects and variance in MMRM with monotone missing data.
- To develop a formula for treatment comparison power using Wald t-test with Kenward-Roger variance.
- To propose a sample size determination procedure for MMRM.
Main Methods:
- Derivation of restricted maximum likelihood (REML) and Kenward-Roger (KR) variance estimators.
- Application to derive power formula for Wald t-test in MMRM.
- Development of a two-step sample size calculation method.
- Simulation studies to evaluate performance under various data distributions and sample sizes.
Main Results:
- Closed-form REML and KR estimators for MMRM with monotone missing data are obtained.
- A formula for power calculation using Wald t-test with KR variance is presented, allowing covariate adjustment without distribution specification.
- A practical two-step sample size determination procedure is proposed.
- Simulations show the method performs well for normal and non-normal data, even with small sample sizes (n=20).
Conclusions:
- The derived estimators and power/sample size formulas provide valuable tools for analyzing longitudinal data in clinical trials.
- The proposed methods are robust to non-normal data and effective in small sample settings.
- This work facilitates more accurate planning and analysis of randomized trials using MMRM.
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