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

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.

使用逆概率加权估计方程对时间固定治疗进行拟合的边际结构模型越来越受欢迎。然而，当数据稀疏时（这是大样本程序的典型情况），所得的效应估计会受到有限样本偏差的影响。在此我们提出一种半贝叶斯估计方法，该方法对估计的模型参数进行惩罚或收缩以提高有限样本的性能。这种方法使用简单的对称数据增广先验。有限的模拟实验表明，当真实值位于先验分布的中心“山丘”内时，所提出的方法可减少有限样本偏差并提高置信区间的覆盖率。我们用一项关于艾滋病治疗的非实验性研究的数据来说明该方法。