A note on compatibility for inference with missing data in the presence of auxiliary covariates

Imputation and inference (or analysis) models that cannot be true simultaneously are frequently used in practice when missing outcomes are present. In these situations, the conclusions can be misleading depending on how “different” the implicit inference model, induced by the imputation model, is from the inference model actually used. We introducemodel ‐based compatibility (MBC) and compare two MBC approaches to a non ‐MBC approach and explore the inferential validity of the latter in a simple case. In addition, we evaluate more complex cases through a series of simulation studies. Overall, we recommend caution when making inferences using a non‐MBC analysis and point out when the inferential “cost” is th e largest.

Source: Statistics in Medicine - November 18, 2018 Category: Statistics Authors: Michael J. Daniels, Xuan Luo Tags: RESEARCH ARTICLE Source Type: research