A self-censoring model for multivariate nonignorable nonmonotone missing data

TL;DR

This paper proposes a novel self-censoring model for multivariate nonignorable missing data, providing identification conditions and semiparametric estimators, with validation through simulations and real data application.

Contribution

It introduces a new self-censoring model for complex missing data, along with doubly robust estimators and identification conditions, advancing analysis of nonmonotone nonignorable missingness.

Findings

Estimators perform well in simulations.

Model effectively analyzes HIV treatment data.

Provides valid inference under partial model misspecification.

Abstract

We introduce a self-censoring model for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome is affected by its own value and is associated with missingness indicators of other outcomes, while conditionally independent of the other outcomes. The self-censoring model complements previous graphical approaches for the analysis of multivariate nonignorable missing data. It is identified under a completeness condition stating that any variability in one outcome can be captured by variability in the other outcomes among complete cases. For estimation, we propose a suite of semiparametric estimators including doubly robust estimators that deliver valid inferences under partial misspecification of the full-data distribution. We evaluate the performance of the proposed estimators with simulations and apply them to analyze a study about the effect of highly active antiretroviral therapy on preterm delivery of HIV-positive mothers.