A Self-Censoring Model for Multivariate Nonignorable Nonmonotone Missing Data
Abstract
We introduce an itemwise modeling approach called “self-censoring” for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome can be affected by its own value and 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 also provide a novel and flexible global sensitivity analysis procedure anchored at the self-censoring. We evaluate the performance of the proposed methods with simulations and apply them to analyze a study about the effect of highly active antiretroviral therapy on preterm delivery of HIV-positive mothers.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 24, 2023
- Source ID
- 10.1111/biom.13916
Entities
People
- Eric J. Tchetgen Tchetgen
- Ilya Shpitser
- Wang Miao
- Y. G. Li
Organizations
- Beijing Municipal Natural Science Foundation
- Johns Hopkins University
- National Institutes of Health
- National Natural Science Foundation of China
- National Science Foundation
- Office of Naval Research
- Peking University
- University of Pennsylvania