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随机缺失下线性混合效应模型的估计与预测方差AI 解读

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,…

分层抽样下差值或比值效应参数的 MOVER 置信区间AI 解读

Stratification is commonly employed in clinical trials to reduce the chance covariate imbalances and increase the precision of the treatment effect estimate. We propose a general framework for constructing the confidence interval (CI) for a difference or ratio effect parameter under stratified sampling by the method of variance estimates recovery (MOVER). We consider the additive variance and additive CI approaches for the difference, in which either the CI for the weighted…