Sequential identification of nonignorable missing data mechanisms

TL;DR

This paper introduces a sequential approach to identify nonignorable missing data mechanisms by constructing models with multiple assumptions, enabling more flexible and identifiable analysis of incomplete data.

Contribution

It proposes a novel sequential method for constructing identifiable nonignorable missing data models using block-specific assumptions.

Findings

Models are non-parametric saturated.

The approach allows different missingness mechanisms for variable blocks.

Ensures models are identifiable from observed data.

Abstract

With nonignorable missing data, likelihood-based inference should be based on the joint distribution of the study variables and their missingness indicators. These joint models cannot be estimated from the data alone, thus requiring the analyst to impose restrictions that make the models uniquely obtainable from the distribution of the observed data. We present an approach for constructing classes of identifiable nonignorable missing data models. The main idea is to use a sequence of carefully set up identifying assumptions, whereby we specify potentially different missingness mechanisms for different blocks of variables. We show that the procedure results in models with the desirable property of being non-parametric saturated.