Zhong Yujie, Cook Richard J
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada N2L 3G1
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada N2L 3G1.
Biostatistics. 2016 Jul;17(3):437-52. doi: 10.1093/biostatistics/kxv054. Epub 2016 Jan 27.
The heritability of chronic diseases can be effectively studied by examining the nature and extent of within-family associations in disease onset times. Families are typically accrued through a biased sampling scheme in which affected individuals are identified and sampled along with their relatives who may provide right-censored or current status data on their disease onset times. We develop likelihood and composite likelihood methods for modeling the within-family association in these times through copula models in which dependencies are characterized by Kendall's [Formula: see text] Auxiliary data from independent individuals are exploited by augmentating composite likelihoods to increase precision of marginal parameter estimates and consequently increase efficiency in dependence parameter estimation. An application to a motivating family study in psoriatic arthritis illustrates the method and provides some evidence of excessive paternal transmission of risk.
通过研究疾病发病时间的家庭内部关联的性质和程度，可以有效地研究慢性病的遗传力。家庭通常是通过一种有偏抽样方案积累的，在该方案中，确定受影响个体并对其进行抽样，同时抽取其亲属，这些亲属可能提供关于其疾病发病时间的右删失数据或当前状态数据。我们开发了似然和复合似然方法，通过copula模型对这些时间的家庭内部关联进行建模，其中依赖性由肯德尔[公式：见正文]来表征。通过增强复合似然来利用来自独立个体的辅助数据，以提高边际参数估计的精度，从而提高依赖性参数估计的效率。对一项关于银屑病关节炎的激励性家庭研究的应用说明了该方法，并提供了一些风险过度父系传递的证据。
