Simulation-based Sensitivity Analysis for Non-ignorable Missing Data

Abstract

Sensitivity analysis is popular in dealing with missing data problems particularly for non-ignorable missingness. It analyses how sensitively the conclusions may depend on assumptions about missing data e.g. missing data mechanism (MDM). We called models under certain assumptions sensitivity models. To make sensitivity analysis useful in practice we need to define some simple and interpretable statistical quantities to assess the sensitivity models. However, the assessment is difficult when the missing data mechanism is missing not at random (MNAR). We propose a novel approach in this paper on attempting to investigate those assumptions based on the nearest-neighbour (KNN) distances of simulated datasets from various MNAR models. The method is generic and it has been applied successfully to several specific models in this paper including meta-analysis model with publication bias, analysis of incomplete longitudinal data and regression analysis with non-ignorable missing covariates.