Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data
Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data
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DOI：
10.1111/j.1541-0420.2012.01754.x
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发表时间：
2012-09-01
期刊：
影响因子：
1.9
通讯作者：
Wang, Mei-Cheng
中科院分区：
文献类型：
--
作者：
Cheng, Yu-Jen;Wang, Mei-Cheng
本文开发了半参数方法估计的倾向分数和因果生存函数的流行生存数据。分析问题出现时，普遍采用的抽样收集故障时间，因此，协变量是不完全观察，由于它们与故障时间。所提出的估计倾向分数的程序共享有趣的功能相似的可能性制定病例对照研究，但在我们的情况下，它需要额外的考虑在截距项。结果表明，在logistic回归设置的校正倾向得分可以通过标准的估计程序与特定的调整截距项。对于因果估计，在我们的模型中遇到了两种不同类型的缺失源：一种可以由潜在结果框架解释;另一种是由流行的抽样方案引起的。统计分析如果不调整两种缺失来源的偏倚，将导致因果推断中的偏倚结果。所提出的方法的部分动机，并应用于监测，流行病学和最终结果（SEER）-医疗保险的诊断为乳腺癌的妇女的相关数据。
This article develops semiparametric approaches for estimation of propensity scores and causal survival functions from prevalent survival data. The analytical problem arises when the prevalent sampling is adopted for collecting failure times and, as a result, the covariates are incompletely observed due to their association with failure time. The proposed procedure for estimating propensity scores shares interesting features similar to the likelihood formulation in case-control study, but in our case it requires additional consideration in the intercept term. The result shows that the corrected propensity scores in logistic regression setting can be obtained through standard estimation procedure with specific adjustments on the intercept term. For causal estimation, two different types of missing sources are encountered in our model: one can be explained by potential outcome framework; the other is caused by the prevalent sampling scheme. Statistical analysis without adjusting bias from both sources of missingness will lead to biased results in causal inference. The proposed methods were partly motivated by and applied to the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data for women diagnosed with breast cancer.