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

Estimating multiple time-fixed treatment effects using a semi-Bayes semiparametric marginal structural Cox proportional hazards regression model.
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Cole SR, Edwards JK, Westreich D, Lesko CR, Lau B, Mugavero MJ, Mathews WC, Eron JJ, Greenland S, ,


Cole SR, Edwards JK, Westreich D, Lesko CR, Lau B, Mugavero MJ, Mathews WC, Eron JJ, Greenland S, , (click to view)

Cole SR, Edwards JK, Westreich D, Lesko CR, Lau B, Mugavero MJ, Mathews WC, Eron JJ, Greenland S, ,

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Biometrical journal. Biometrische Zeitschrift 2017 10 27() doi 10.1002/bimj.201600140

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.

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