Kim Ryung S
Department of Epidemiology and Population Health, Albert Einstein College of Medicine.
Commun Stat Appl Methods. 2013 Nov;20(6):455-466. doi: 10.5351/CSAM.2013.20.6.455.
In nested case-control studies, the most common way to make inference under a proportional hazards model is the conditional logistic approach of Thomas (1977). Inclusion probability methods are more efficient than the conditional logistic approach of Thomas; however, the epidemiology research community has not accepted the methods as a replacement of the Thomas' method. This paper promotes the inverse probability weighting method originally proposed by Samuelsen (1997) in combination with an approximate jackknife standard error that can be easily computed using existing software. Simulation studies demonstrate that this approach yields valid type 1 errors and greater powers than the conditional logistic approach in nested case-control designs across various sample sizes and magnitudes of the hazard ratios. A generalization of the method is also made to incorporate additional matching and the stratified Cox model. The proposed method is illustrated with data from a cohort of children with Wilm's tumor to study the association between histological signatures and relapses.
在巢式病例对照研究中，在比例风险模型下进行推断的最常见方法是托马斯（1977年）提出的条件逻辑回归方法。包含概率方法比托马斯的条件逻辑回归方法更有效；然而，流行病学研究界尚未接受这些方法来替代托马斯方法。本文推广了萨缪尔森（1997年）最初提出的逆概率加权方法，并结合了一种近似刀切法标准误差，该误差可使用现有软件轻松计算。模拟研究表明，在各种样本量和风险比大小的巢式病例对照设计中，该方法产生的一类错误有效，且比条件逻辑回归方法具有更大的检验效能。该方法还进行了推广，以纳入额外的匹配和分层Cox模型。本文用一组患威尔姆斯瘤儿童的数据说明了所提出的方法，以研究组织学特征与复发之间的关联。
