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Statistical Methods for The Analysis of Censored Family Data under Biased Sampling Schemes

Tuesday, February 13, 2018 -
11:15 to 12:15
Yujie Zhong, MRC Biostatistics Unit, University of Cambridge
Room 4192, Earth Sciences Building (2207 Main Mall) - Video Conference

*Please note unusual start-time of talk.

Studies of the genetic basis for chronic disease often first aim to examine the nature and extend of within-family dependence in disease status. Families for such studies are typically selected using a biased sampling scheme in which affected individuals are recruited from a disease registry, followed by their consenting relatives. This gives right-censored or current status information on disease onset times.  Methods for correcting this response-dependent sampling scheme have been developed for correlated binary data but variation in the age of assessment for family members makes this analysis uninterpretable.  We develop likelihood and composite likelihood methods for modeling within-family associations in disease onset time using copula functions and second-order regression models in which dependencies are characterized by Kendall’s τ. Auxiliary data from an independent sample of individuals can be integrated by augmenting the composite likelihood to ensure identifiability and increase efficiency. An application to a motivating family study in psoriatic arthritis illustrates the method and provides evidence of excessive paternal transmission of risk.  Ongoing work on the use of second-order estimating functions, alternative framework for dependence modeling, and approaches to efficient study design will also be discussed.