Comment: Incomplete Data in Clinical Studies: Analysis, Sensitivity, and Sensitivity Analysis

摘要

I would like to congratulate Dr. Molenberghs for writing an excellent article that gives a comprehensive overview of so many important issues in missing data, especially nonignorable missing data in longitudinal studies.The article nicely examines the three basic approaches to nonignorable missing data modeling, these being selection models (SeMs), pattern mixture models (PMMs), and shared parameter models. Similarities, motivations, contrasts, and connections between these three modeling methods are very nicely made throughout the article. A comprehensive review of modeling and inference issues is given for these classes of models.One of the most important issues discussed in the article is the notion that for every missing not at random (MNAR) model is a missing at random (MAR) counterpart. This is an interesting and important finding. The MAR counterpart given in Eq. 13 for the PMM corresponds to a mixture distribution resulting from summing the last component of Eq. 12 with respect to the