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A self-censoring model for multivariate nonignorable nonmonotone missing data.

Yilin Li1, Wang Miao1, Ilya Shpitser2

  • 1Department of Probability and Statistics, Peking University, Beijing, China.

Biometrics
|July 25, 2023
PubMed
Summary

We developed "self-censoring," a new statistical model for handling complex missing data in multiple variables. This approach improves analysis accuracy for multivariate nonignorable nonmonotone missing data.

Keywords:
doubly robust estimationidentificationmissing not at randomnonmonotone missingness

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Multivariate data often exhibit complex missingness patterns.
  • Nonignorable and nonmonotone missing data pose significant analytical challenges.
  • Existing graphical methods may not fully address these complexities.

Purpose of the Study:

  • To introduce a novel itemwise modeling approach,
  • self-censoring
  • , for multivariate nonignorable nonmonotone missing data.
  • To provide semiparametric estimators and a global sensitivity analysis procedure.
  • To demonstrate the utility of the self-censoring model in a real-world health study.

Main Methods:

  • Developed the "self-censoring" model where each outcome's missingness depends on its own value and other outcomes' missingness indicators.
  • Proposed semiparametric and doubly robust estimators for parameter estimation.
  • Introduced a global sensitivity analysis procedure specific to the self-censoring model.
  • Validated methods through simulation studies and application to HIV/ART data.

Main Results:

  • The self-censoring model is identified under a completeness condition.
  • Doubly robust estimators ensure valid inferences even with partial model misspecification.
  • The sensitivity analysis provides a flexible tool for assessing missing data impact.
  • The model was successfully applied to analyze the effect of highly active antiretroviral therapy on preterm delivery.

Conclusions:

  • The self-censoring approach offers a robust framework for analyzing multivariate nonignorable nonmonotone missing data.
  • The proposed estimation and sensitivity analysis methods are effective and flexible.
  • This methodology enhances the analysis of complex health-related datasets.