Doubly Robust Estimator for Net Survival Rate in Analyses of Cancer Registry Data
Doubly Robust Estimator for Net Survival Rate in Analyses of Cancer Registry Data
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DOI：
10.1111/biom.12568
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发表时间：
2017-03-01
期刊：
影响因子：
1.9
通讯作者：
Hattori, Satoshi
中科院分区：
文献类型：
--
作者：
Komukai, Sho;Hattori, Satoshi
基于癌症登记数据库的癌症人口研究被广泛地用于解决各种研究问题。一般来说，癌症登记数据库不收集死亡原因的信息。净存活率是指受试者不会因癌症以外的任何原因死亡的存活率。这种反事实的概念被广泛用于癌症登记数据的分析。Perme， Stare和Esteve（2012）在假设审查时间与生存时间和协变量无关的情况下，提出了净存活率的非参数估计。Kodre和Perme（2013）提出了协变量相关审查下净存活率的逆加权估计器。在协变量相关的审查下估计净存活率的另一种方法是对给定协变量的条件净存活率应用回归模型。在本文中，我们提出了一种新的净存活率估计方法。所提出的估计量被证明是双重鲁棒的，因为它在生存时间和审查时间的回归模型中至少有一个是一致的。我们通过渐近理论和模拟研究来检验我们提出的估计量的理论和经验性质。我们还将提出的方法应用于日本大阪胃癌患者的癌症登记数据。
Cancer population studies based on cancer registry databases are widely conducted to address various research questions. In general, cancer registry databases do not collect information on cause of death. The net survival rate is defined as the survival rate if a subject would not die for any causes other than cancer. This counterfactual concept is widely used for the analyses of cancer registry data. Perme, Stare, and Esteve (2012) proposed a nonparametric estimator of the net survival rate under the assumption that the censoring time is independent of the survival time and covariates. Kodre and Perme (2013) proposed an inverse weighting estimator for the net survival rate under the covariate-dependent censoring. An alternative approach to estimating the net survival rate under covariate-dependent censoring is to apply a regression model for the conditional net survival rate given covariates. In this article, we propose a new estimator for the net survival rate. The proposed estimator is shown to be doubly robust in the sense that it is consistent at least one of the regression models for survival time and for censoring time. We examine the theoretical and empirical properties of our proposed estimator by asymptotic theory and simulation studies. We also apply the proposed method to cancer registry data for gastric cancer patients in Osaka, Japan.