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Doubly robust estimation and sensitivity analysis for marginal structural quantile models.

Chao Cheng1,2, Liangyuan Hu3, Fan Li1,2,4

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, United States.

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|June 17, 2024
PubMed
Summary
This summary is machine-generated.

The marginal structure quantile model (MSQM) offers new insights into time-varying treatment effects on outcomes. A novel doubly robust estimator enhances causal inference accuracy and robustness, even with potential model misspecification.

Keywords:
causal inferencedouble robustnessefficient influence functioninverse probability weightingquantile causal effectunmeasured confounding

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Area of Science:

  • Causal Inference
  • Semiparametric Statistics
  • Biostatistics

Background:

  • Understanding time-varying treatment effects on the entire outcome distribution is crucial.
  • Existing methods may lack robustness or efficiency in complex scenarios.
  • The marginal structure quantile model (MSQM) framework offers a promising approach.

Purpose of the Study:

  • To develop a novel, doubly robust estimator for the MSQM.
  • To enhance causal inference for time-varying treatments.
  • To assess robustness to model misspecification and unmeasured confounding.

Main Methods:

  • Derivation of the efficiency influence function for MSQM.
  • Proposal of a doubly robust estimator based on treatment assignment and outcome models.
  • Implementation using smoothed estimating equations.
  • Development of a confounding function approach for sensitivity analysis.

Main Results:

  • The doubly robust estimator is consistent under partial model correctness and semiparametric efficient if both models are correct.
  • The proposed methods demonstrate good finite-sample performance in simulations.
  • Application to electronic health record data reveals insights into antihypertensive medication effects.

Conclusions:

  • The doubly robust MSQM estimator provides a robust and efficient tool for causal inference with time-varying treatments.
  • The confounding function approach aids in assessing sensitivity to unmeasured confounding.
  • The methods are applicable to real-world health data for treatment effect evaluation.