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Updated: May 31, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Enhancing propensity score analysis with data missing not at random: Introducing dual-forest proximity imputation.

Yongseok Lee1, Walter L Leite2

  • 1Department of Human Development and Family Science, Purdue University, Hanley Hall (Room 356), 1202 Mitch Daniels Blvd, West Lafayette, IN, 47906, USA. lee5321@purdue.edu.

Behavior Research Methods
|May 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dual-forest proximity imputation method to improve propensity score analysis (PSA) for missing data not at random (MNAR). The new approach enhances bias reduction in treatment effect estimation.

Keywords:
Dual-forest proximity imputationMissing not at randomMultiple imputationPropensity score analysisRandom forests

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Propensity score analysis (PSA) is crucial for estimating treatment effects in observational studies.
  • Handling missing data, especially missing not at random (MNAR), presents significant challenges in PSA.
  • Current methods often rely on logistic regression, which requires manual functional form specification and struggles with numerous covariates.

Purpose of the Study:

  • To propose novel methods for PSA with MNAR data that overcome limitations of existing logistic regression-based approaches.
  • To introduce a dual-forest proximity imputation method leveraging random forest techniques.
  • To evaluate the performance of the proposed method in reducing bias for treatment effect estimation.

Main Methods:

  • Replacement of logistic regression with random forest models for handling MNAR data mechanisms.
  • Development of a dual-forest proximity imputation method using two proximity matrices from random forests.
  • Incorporation of missingness pattern information within the imputation matrices.

Main Results:

  • Monte Carlo simulations demonstrate superior bias reduction of dual-forest proximity imputation compared to existing and alternative methods.
  • The proposed method effectively handles various types of MNAR mechanisms.
  • A case study using the National Longitudinal Survey of Youth (NLSY79) data illustrates practical application.

Conclusions:

  • The dual-forest proximity imputation method offers a robust and effective alternative for PSA with MNAR data.
  • This approach simplifies implementation and improves bias reduction, particularly with complex datasets.
  • The findings have significant implications for researchers analyzing secondary data with missingness.