Comparative effectiveness research often involves evaluating the differences in the risks of an event of interest between two or more treatments using observational data. Often, the post-treatment outcome of interest is whether the event happens within a pre-specified time window, which leads to a binary outcome. One source of bias for estimating the causal treatment effect is the presence of confounders, which are usually controlled using propensity score-based methods. An additional source of bias is right-censoring, which occurs when the information on the outcome of interest is not completely available due to dropout, study termination, or treatment switch before the event of interest. We propose an inverse probability weighted regression-based estimator that can simultaneously handle both confounding and right-censoring, calling the method CIPWR, with the letter C highlighting the censoring component. CIPWR estimates the average treatment effects by averaging the predicted outcomes obtained from a logistic regression model that is fitted using a weighted score function. The CIPWR estimator has a double robustness property such that estimation consistency can be achieved when either the model for the outcome or the models for both treatment and censoring are correctly specified. We establish the asymptotic properties of the CIPWR estimator for conducting inference, and compare its finite sample performance with that of several alternatives through simulation studies. The methods under comparison are applied to a cohort of prostate cancer patients from an insurance claims database for comparing the adverse effects of four candidate drugs for advanced stage prostate cancer.
翻译:比较有效性研究往往涉及评估两种或两种以上治疗使用观察数据的感兴趣事件的风险差异。通常,后处理的结果是该事件是否发生在预先指定的时间窗口内,从而导致二进制结果。估计因果治疗效果的一个偏差来源是存在困惑者,他们通常使用偏向性分分法加以控制。另一个偏差来源是右检查,这是在以下情况下发生的:利息结果信息因辍学、研究终止、或事前治疗转换而不能完全获得。我们提议了一个反概率加权回归估计器,可以同时处理混结和右检查两种情况,称为CIPWR方法,用C信着重检查部分。CIPWR估计了平均从使用加权得分数功能的后勤回归模型获得的预测结果的平均治疗效果。CIPWR估算器具有双重稳健性,因此,当结果模型或预选的坏结果模型用于预估结果和预估结果时,可以同时处理并同时使用右位检验方法。在对四期的癌症进行对比性研究中,我们正确地规定,对平均治疗和预估性癌症研究的样本进行定期分析。