Pattern-mixture models stratify incomplete data by the pattern of missing values and formulate distinct models within each stratum. Pattern-mixture models are developed for analyzing a random sample on continuous variables y$_{(1)}$, y$_{(2)}$ when values of y$_{(2)}$ are nonrandomly missing. Methods for scalar y$_{(1)}$ and y$_{(2)}$ are here generalized to vector y$_{(1)}$ and y$_{(2)}$ with additional… Continue reading Pattern-Mixture Models for Multivariate Incomplete Data with Covariates