Falcaro Milena, Pickles Andrew
Biostatistics Group, Division of Epidemiology and Health Sciences, The University of Manchester, UK.
Stat Med. 2007 Feb 10;26(3):663-80. doi: 10.1002/sim.2522.
We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific.
我们专注于对具有高度结构化相互依存关系且受区间删失影响的多变量生存时间进行分析。这类数据在发育遗传学和遗传流行病学中很常见。我们提出了一种灵活的混合概率单位模型，该模型能自然地处理复杂但无信息的删失情况。记录的发病年龄被视为可能被删失的有序结局，区间删失机制被视为源于对一个连续变量的粗化测量，该连续变量被观察到落在个体特定阈值之间。这绕过了对失效时间需被观察到落入非重叠区间的要求。通过在标准概率单位模型中嵌入一个多变量Box-Cox变换，其参数与模型的其他参数联合估计，从而放宽了对发病年龄正态分布的假设。对转换后的发病年龄的潜在多变量正态协方差矩阵进行复杂分解成为可能。这里将新方法应用于一项关于双胞胎未经父母许可首次使用烟草和首次饮酒年龄的多变量研究。所提出的模型允许估计这两种风险行为共同具有的遗传和环境效应以及特定的效应。
