Abstract:
:In genetic association studies, mixed effects models have been widely used in detecting the pleiotropy effects which occur when one gene affects multiple phenotype traits. In particular, bivariate mixed effects models are useful for describing the association of a gene with a continuous trait and a binary trait. However, such models are inadequate to feature the data with response mismeasurement, a characteristic that is often overlooked. It has been well studied that in univariate settings, ignorance of mismeasurement in variables usually results in biased estimation. In this paper, we consider the setting with a bivariate outcome vector which contains a continuous component and a binary component both subject to mismeasurement. We propose an induced likelihood approach and an EM algorithm method to handle measurement error in continuous response and misclassification in binary response simultaneously. Simulation studies confirm that the proposed methods successfully remove the bias induced from the response mismeasurement.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Zhang Q,Yi GYdoi
10.1002/sim.8688subject
Has Abstractpub_date
2020-11-20 00:00:00pages
3700-3719issue
26eissn
0277-6715issn
1097-0258journal_volume
39pub_type
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