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Second-order estimating equations for clustered current status data from family studies using response-dependent sampling

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Zhong, Y 
Cook, RJ 


Studies about the genetic basis for disease are routinely conducted through family studies under response-dependent sampling in which affected individuals called probands are sampled from a disease registry, and their respective family members (non-probands) are recruited for study. The extent to which the dependence in some feature of the disease process (e.g. presence, age of onset, severity) varies according to the kinship of individuals, reflects the evidence of a genetic cause for disease. When the probands are selected from a disease registry it is common for them to provide quite detailed information regarding their disease history, but non-probands often simply provide their disease status at the time of contact. We develop conditional second-order estimating equations for studying the nature and extent of within-family dependence which recognizes the biased sampling scheme employed in family studies and the current status data provided by the non-probands. Simulation studies are carried out to evaluate the finite sample performance of different estimating functions and to quantify the empirical relative effciency of the various methods. Sensitivity to model misspecification is also explored. An application to a motivating psoriatic arthritis family study is given for illustration.



current status data, family study, gaussian copula, relative efficiency, response-dependent sampling, robustness, second-order estimating equations

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Statistics in Biosciences

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Springer Nature
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The authors thank Drs. Dafna Gladman, Vinod Chandran and Lihi Eder for stimulating collaboration and helpful discussions involving the psoriatic arthritis research program. This research was nancially supported by grants from the UK Medical Research Council [Unit programme no. MC UP 1302/3], the Natural Sciences and Engineer- ing Research Council of Canada (RGPIN 155849) and the Canadian Institutes for Health Research (FRN 13887). Richard Cook is a Tier I Canada Research Chair in Statistical Methods for Health Research.