Analyses under Missing‐not‐at‐random Assumptions

This chapter discusses ways of implementing the assumption that data are not missing at random (missing-not-at-random (MNAR)), with an emphasis on using pattern-mixture models (PMMs) to do this. It first discusses the background to sensitivity analyses and PMMs. Two methods that use multiple imputation (MI) to implement sensitivity analyses with PMMs are then described and compared. The second par…