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Estimating multiple time‐fixed treatment effects using a semi‐Bayes semiparametric marginal structural Cox proportional hazards regression model
Author(s) -
Cole Stephen R.,
Edwards Jessie K.,
Westreich Daniel,
Lesko Catherine R.,
Lau Bryan,
Mugavero Michael J.,
Mathews W. Christopher,
Eron Joseph J.,
Greenland Sander
Publication year - 2018
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201600140
Subject(s) - prior probability , mathematics , statistics , bayes' theorem , confidence interval , proportional hazards model , sample size determination , marginal distribution , marginal likelihood , sample (material) , marginal structural model , econometrics , bayesian probability , random variable , chromatography , chemistry
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.