Estimating multiple time‐fixed treatment effects using a semi‐Bayes semiparametric marginal structural Cox proportional hazards regression model. Issue 1 (27th October 2017)

• Record Type: Journal Article
• Authors: Cole, Stephen R.; Edwards, Jessie K.; Westreich, Daniel; Lesko, Catherine R.; Lau, Bryan; Mugavero, Michael J.; Mathews, W. Christopher; Eron, Joseph J.; Greenland, Sander
• Is Part Of: Biometrical journal. Volume 60:Issue 1(2018:Jan.)
• Page Start: 100
• Page End: 114
• Publication Date: 2017-10-27
• Subjects: bias -- causal inference -- cohort study -- semi‐Bayes -- semiparametric -- survival analysis; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201600140
• Languages: English
• ISSNs: 0323-3847

Abstract

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.