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Marginal Structural Cox Models with Case-Cohort Sampling.

Hana Lee1, Michael G Hudgens2, Jianwen Cai2

  • 1Brown University.

Statistica Sinica
|April 9, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for estimating causal effects in survival analysis using case-cohort studies. The approach uses inverse probability weighting within marginal structural models for accurate time-varying treatment effect assessment.

Keywords:
Case-cohort StudyCausal InferenceCox ModelMarginal Structural ModelSurvival Analysis

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Assessing time-varying treatment effects on survival is crucial in biomedical research.
  • Time-varying confounders complicate causal effect estimation.
  • Marginal structural models (MSMs) with inverse probability weighting offer a solution for consistent estimation.

Purpose of the Study:

  • To develop and evaluate a method for estimating causal effects from semiparametric marginal structural Cox models (MSCMs) in case-cohort studies.
  • To address the challenges of analyzing time-varying treatments and confounders in cost-effective case-cohort designs.
  • To provide a statistically sound approach for estimating causal hazard ratios.

Main Methods:

  • Utilized case-cohort sampling for efficient data collection in large studies.
  • Applied inverse probability weighting within the marginal structural Cox model framework.
  • Employed a weighted-pseudo-partial-likelihood maximization for parameter estimation.

Main Results:

  • The proposed weighted-pseudo-partial-likelihood estimator is consistent and asymptotically normal under stated conditions.
  • Simulation studies demonstrated the finite sample performance of the estimator.
  • The methodology allows for estimation of causal hazard ratios from MSCMs.

Conclusions:

  • The developed method provides a valid approach for estimating causal effects in case-cohort studies with time-varying treatments and confounders.
  • This technique enhances the utility of cost-effective case-cohort designs for causal inference in survival analysis.
  • The study offers practical guidance for computation using standard survival analysis software.