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Accuracy of conventional and marginal structural Cox model estimators: a simulation study.

Yongling Xiao1, Michal Abrahamowicz, Erica E M Moodie

  • 1McGill University, Canada.

The International Journal of Biostatistics
|October 5, 2011
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Summary

This study introduces a direct method for fitting marginal structural Cox models (MSM) for time-dependent confounding. The research evaluates this direct approach against traditional methods using simulations and proposes novel normalized weights to improve estimator stability.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Survival Analysis

Background:

  • Marginal structural models (MSM) are crucial for handling time-dependent confounding in treatment effect estimation.
  • Traditional MSM fitting often relies on pooled logistic regression, which can have limitations.

Purpose of the Study:

  • To demonstrate the direct fitting of marginal structural Cox models (MSM) using standard software.
  • To evaluate the performance of direct MSM fitting against conventional methods via simulation.
  • To propose and assess novel normalized weights for reducing MSM estimator variability.

Main Methods:

  • Implementation of a weighted Cox proportional hazards function for direct MSM fitting.
  • Simulation studies using two data-generating models, including one with time-dependent confounders and time-varying treatment.
  • Comparison of direct MSM, pooled logistic regression MSM, and conventional time-dependent Cox models.
  • Evaluation of unstabilized, stabilized, and two novel normalized weights.

Main Results:

  • The direct fitting of marginal structural Cox models is feasible using existing software.
  • Simulation results highlight limitations of conventional time-dependent Cox models and pooled logistic regression-based MSMs.
  • Proposed normalized weights show potential for reducing the variability of MSM estimators.

Conclusions:

  • Direct fitting of marginal structural Cox models offers a viable alternative to pooled logistic regression.
  • Novel normalized weights can enhance the precision of marginal structural model estimates in survival analysis.
  • The study provides valuable insights into robust methods for causal inference with time-dependent confounding.