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

Biom J. 2018 Jan;60(1):100-114. doi: 10.1002/bimj.201600140. Epub 2017 Oct 27.


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.

Keywords: bias; causal inference; cohort study; semi-Bayes; semiparametric; survival analysis.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anti-HIV Agents / therapeutic use
  • Bayes Theorem
  • Biometry / methods*
  • HIV Infections / drug therapy
  • Humans
  • Models, Statistical*
  • Proportional Hazards Models
  • Regression Analysis


  • Anti-HIV Agents