title: Augmented composite likelihood for copula modeling in family studies under biased sampling.
abstract: 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.
authors.affiliation: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada N2L 3G1 zyujie@uwaterloo.ca.
authors.forename: Yujie
authors.initials: Y
authors.lastname: Zhong
authors.affiliation: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada N2L 3G1.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Cook
mesh_terms: D002908:Chronic Disease / Q000453:epidemiology*; D005190:Family; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D015983:Selection Bias
keywords: Auxiliary data; Biased sampling; Composite likelihood; Family study; Gaussian copula