Dixon Stephanie N, Darlington Gerarda A, Desmond Anthony F
Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, ON N6A 5C1, Canada.
Lifetime Data Anal. 2011 Oct;17(4):473-95. doi: 10.1007/s10985-011-9198-9. Epub 2011 May 21.
Family-based follow-up study designs are important in epidemiology as they enable investigations of disease aggregation within families. Such studies are subject to methodological complications since data may include multiple endpoints as well as intra-family correlation. The methods herein are developed for the analysis of age of onset with multiple disease types for family-based follow-up studies. The proposed model expresses the marginalized frailty model in terms of the subdistribution hazards (SDH). As with Pipper and Martinussen's (Scand J Stat 30:509-521, 2003) model, the proposed multivariate SDH model yields marginal interpretations of the regression coefficients while allowing the correlation structure to be specified by a frailty term. Further, the proposed model allows for a direct investigation of the covariate effects on the cumulative incidence function since the SDH is modeled rather than the cause specific hazard. A simulation study suggests that the proposed model generally offers improved performance in terms of bias and efficiency when a sufficient number of events is observed. The proposed model also offers type I error rates close to nominal. The method is applied to a family-based study of breast cancer when death in absence of breast cancer is considered a competing risk.
基于家庭的随访研究设计在流行病学中很重要,因为它们能够对家庭内部的疾病聚集情况进行调查。这类研究存在方法学上的复杂性,因为数据可能包括多个终点以及家庭内部的相关性。本文所提出的方法是为基于家庭的随访研究中多种疾病类型的发病年龄分析而开发的。所提出的模型根据子分布风险(SDH)来表达边缘化脆弱模型。与Pipper和Martinussen(《斯堪的纳维亚统计杂志》30:509 - 521,2003)的模型一样,所提出的多变量SDH模型在允许通过脆弱项指定相关结构的同时,对回归系数产生边际解释。此外,由于对SDH进行了建模而非特定病因风险,所提出的模型允许直接研究协变量对累积发病率函数的影响。一项模拟研究表明,当观察到足够数量的事件时,所提出的模型在偏差和效率方面通常具有更好的表现。所提出的模型还提供接近名义水平的I型错误率。该方法应用于一项基于家庭的乳腺癌研究,其中将无乳腺癌情况下的死亡视为竞争风险。