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Semiparametric transformation models for causal inference in time to event studies with all-or-nothing compliance.
The Journal of the Royal Statistical Society, Series B (Statistical Methodology) ( IF 5.8 ) Pub Date : 2015-04-15 , DOI: 10.1111/rssb.12072
Wen Yu 1 , Kani Chen 2 , Michael E Sobel 3 , Zhiliang Ying 3
Affiliation  

We consider causal inference in randomized survival studies with right censored outcomes and all-or-nothing compliance, using semiparametric transformation models to estimate the distribution of survival times in treatment and control groups, conditional on covariates and latent compliance type. Estimands depending on these distributions, for example, the complier average causal effect (CACE), the complier effect on survival beyond time t, and the complier quantile effect are then considered. Maximum likelihood is used to estimate the parameters of the transformation models, using a specially designed expectation-maximization (EM) algorithm to overcome the computational difficulties created by the mixture structure of the problem and the infinite dimensional parameter in the transformation models. The estimators are shown to be consistent, asymptotically normal, and semiparametrically efficient. Inferential procedures for the causal parameters are developed. A simulation study is conducted to evaluate the finite sample performance of the estimated causal parameters. We also apply our methodology to a randomized study conducted by the Health Insurance Plan of Greater New York to assess the reduction in breast cancer mortality due to screening.

中文翻译:

半参数化转换模型,用于按时间进行事件全因或全无合规性研究的因果推理。

我们在具有正确审查结果和全有或全无依从性的随机生存研究中考虑因果推理,使用半参数转换模型来估计治疗组和对照组的生存时间分布,取决于协变量和潜在依从类型。然后考虑取决于这些分布的估计值,例如,合规者平均因果效应(CACE),对超过时间t的生存的合规效应以​​及合规分位数效应。最大似然用于估计转换模型的参数,使用经过特殊设计的期望最大化(EM)算法来克服问题的混合结构和转换模型中无穷维参数所产生的计算困难。估计量是一致的,渐近正态和半参数有效。开发了因果参数的推论程序。进行了仿真研究,以评估估计的因果参数的有限样本性能。我们还将我们的方法应用于大纽约健康保险计划进行的一项随机研究,以评估因筛查而降低的乳腺癌死亡率。
更新日期:2019-11-01
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