Estimating multiple time-fixed treatment effects using a semi-Bayes semiparametric marginal structural Cox proportional hazards regression model.

Academic Article


  • Marginal structural models for time-fixed treatments fit using inverse-probability weighted estimating equations are increasingly popular. Nonetheless, the resulting effect estimates are subject to finite-sample bias when data are sparse, as is typical for large-sample procedures. Here we propose a semi-Bayes estimation approach which penalizes or shrinks the estimated model parameters to improve finite-sample performance. This approach uses simple symmetric data-augmentation priors. Limited simulation experiments indicate that the proposed approach reduces finite-sample bias and improves confidence-interval coverage when the true values lie within the central "hill" of the prior distribution. We illustrate the approach with data from a nonexperimental study of HIV treatments.
  • Published In


  • bias, causal inference, cohort study, semi-Bayes, semiparametric, survival analysis, Anti-HIV Agents, Bayes Theorem, Biometry, HIV Infections, Humans, Models, Statistical, Proportional Hazards Models, Regression Analysis
  • Digital Object Identifier (doi)

    Author List

  • Cole SR; Edwards JK; Westreich D; Lesko CR; Lau B; Mugavero MJ; Mathews WC; Eron JJ; Greenland S; CNICS Investigators
  • Start Page

  • 100
  • End Page

  • 114
  • Volume

  • 60
  • Issue

  • 1