Selection Models and Pattern‐Mixture Models for Incomplete Data with Covariates
Bart Michiels, Geert Molenberghs, Stuart R. Lipsitz · Biometrics · 1999
Most models for incomplete data are formulated within the selection model framework. This paper studies similarities and differences of modeling incomplete data within both selection and pattern-mixture settings. The focus is on missing at random mechanisms and on categorical data. Point and interval estimation is discussed. A comparison of both approaches is done on side effects in a psychiatric study.