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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Censored quantile regression based on multiply robust propensity scores.

Xiaorui Wang1, Guoyou Qin2, Xinyuan Song3

  • 112655Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, China.

Statistical Methods in Medical Research
|December 14, 2021
PubMed
Summary

This study introduces a novel multiply robust propensity score method for censored quantile regression, improving resistance to model misspecification. The new approach enhances estimation accuracy in survival data analysis.

Keywords:
Censored quantile regressionhuman immunodeficiency virusesinformative subsetmultiply robustpropensity score

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Censored quantile regression is crucial for analyzing survival data with incomplete observations.
  • Existing methods often rely on propensity scores, which are susceptible to parametric model misspecification or the curse of dimensionality in nonparametric approaches.

Purpose of the Study:

  • To propose a new multiply robust propensity score estimation method for censored quantile regression.
  • To enhance robustness against propensity score model misspecification.
  • To investigate the theoretical properties and practical performance of the proposed method.

Main Methods:

  • Development of a multiply robust propensity score estimator.
  • Theoretical analysis of the estimator's consistency and asymptotic normality.
  • Simulation studies to evaluate performance compared to existing methods.
  • Application to a human immunodeficiency virus (HIV) study.

Main Results:

  • The proposed multiply robust method demonstrates resistance to misspecification of individual propensity score models.
  • Theoretical properties (consistency, asymptotic normality) are established.
  • Simulation studies indicate favorable performance of the new estimator.

Conclusions:

  • The multiply robust propensity score approach offers a valuable alternative for censored quantile regression.
  • It provides improved reliability when the true propensity score model is unknown.
  • The method is applicable to real-world survival data analysis, such as in HIV research.