Augmented composite likelihood for copula modeling in family studies under biased sampling
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Date
2016-01-27
Authors
Zhong, Yujie
Cook, Richard J.
Journal Title
Journal ISSN
Volume Title
Publisher
Oxford University Press
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 τ. 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.
Description
This is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The version of record Zhong Y and Cook RJ (2016). Biostatistics, 17(3): 437–452. This article is available online at: DOI:10.1093/biostatistics/kxv054.
Keywords
Auxiliary data, Biased sampling, Composite likelihood, Family study, Gaussian copula