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

for the CNICS Investigators

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)100-114
Number of pages15
JournalBiometrical Journal
Volume60
Issue number1
DOIs
StatePublished - Jan 2018

Keywords

  • bias
  • causal inference
  • cohort study
  • semi-Bayes
  • semiparametric
  • survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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