Accounting for not-at-random missingness through imputation stacking

TL;DR

This paper introduces a novel weighted analysis method for handling not-at-random missing data after multiple imputation, enhancing robustness in health research studies.

Contribution

It proposes a new approach using weights in stacked imputations to address not-at-random missingness, including a sensitivity analysis framework and an R package for implementation.

Findings

Method performs well when the missingness model is correct

Allows sensitivity analysis for different missingness assumptions

Applied successfully to HPV test data in cancer survival study

Abstract

Not-at-random missingness presents a challenge in addressing missing data in many health research applications. In this paper, 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 StackImpute to facilitate implementation as part of routine sensitivity analyses. We apply the proposed method to account for not-at-random missingness in human papillomavirus test results in a study of survival for patients diagnosed with oropharyngeal cancer.