Variance for Estimation and Prediction in Linear Mixed Effects Models Under MAR

In linear mixed effects models (LMM), inference on fixed effects typically relies on either the generalized least squares (GLS) variance estimator obtained by assuming that the variance parameters are known, or on the Kenward-Roger method with a small-sample bias adjustment.

A simulation study shows that both approaches can underestimate the variance of the treatment effect and inflate the type I error rate in large clinical trials under missing at random (MAR) mechanisms, with greater type I error inflation as the differences in the missingness mechanisms between treatment groups increase.

Counterintuitively, the estimated fixed effects and variance parameters exhibit asymptotic dependence.

Ignoring this dependence may lead to invalid inference.

To account for this additional source of variability, we propose a new asymptotic variance estimator and a small-sample bias-corrected estimator to quantify uncertainty in estimation and prediction in LMM under MAR.

The proposed methods are theoretically justified, and their performance is demonstrated by numerical examples.