Closed-form REML estimators and sample size determination for mixed effects models for repeated measures under monotone missingness
Related Experiment Video
Updated: Mar 7, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
1Shire, 300 Shire Way, Lexington, 02421, MA, U.S.A.
Abstract:
We derive the closed-form restricted maximum likelihood estimator and Kenward-Roger's variance estimator for fixed effects in the mixed effects model for repeated measures (MMRM) when the missing data pattern is monotone. As an important application of the analytic result, we present the formula for calculating the power of treatment comparison using the Wald t-test with the Kenward-Roger adjusted variance estimate in MMRM. It allows adjustment for baseline covariates without the need to specify the covariate distribution in randomized trials. A simple two-step procedure is proposed to determine the sample size needed to achieve the targeted power. The proposed method performs well for both normal and moderately non-normal data even in small samples (n=20) in simulations. An antidepressant trial is analyzed for illustrative purposes. Copyright © 2017 John Wiley & Sons, Ltd.
Related Concept Videos
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Kaplan-Meier Approach
What are Estimates?
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
Censoring Survival Data

