Format

Send to

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

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

Author information

1
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.
2
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
3
Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
4
Department of Medicine, School of Medicine, University of California, San Diego, CA, USA.
5
Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
6
Departments of Epidemiology and Statistics, UCLA, Los Angeles, CA, USA.

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.

KEYWORDS:

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

PMID:
29076182
DOI:
10.1002/bimj.201600140
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center