Accounting for not-at-random missingness through imputation stacking.

ABSTRACT: Not-at-random missingness presents a challenge in addressing missing data in many health research applications. In this article, we propose a new approach to account for not-at-random missingness after multiple imputation through weighted analysis of stacked multiple imputations. The weights are easily calculated as a function of the imputed data and assumptions about the not-at-random missingness. We demonstrate through simulation that the proposed method has excellent performance when the missingness model is correctly specified. In practice, the missingness mechanism will not be known. We show how we can use our approach in a sensitivity analysis framework to evaluate the robustness of model inference to different assumptions about the missingness mechanism, and we provide R package Sta

SUBMITTER: Beesley LJ 

PROVIDER: S-EPMC8595557 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature