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Estimating multiple time‐fixed treatment effects using a semi‐Bayes semiparametric marginal structural Cox proportional hazards regression model |
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| Authors: | Stephen R. Cole Jessie K. Edwards Daniel Westreich Catherine R. Lesko Bryan Lau Michael J. Mugavero W. Christopher Mathews Joseph J. Eron Jr. Sander Greenland for the CNICS Investigators |
| Affiliation: | 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 |