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http://dx.doi.org/10.5351/KJAS.2020.33.2.123

Joint analysis of binary and continuous data using skewed logit model in developmental toxicity studies  

Kim, Yeong-hwa (Department of Applied Statistics, Chung-Ang University)
Hwang, Beom Seuk (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.33, no.2, 2020 , pp. 123-136 More about this Journal
Abstract
It is common to encounter correlated multiple outcomes measured on the same subject in various research fields. In developmental toxicity studies, presence of malformed pups and fetal weight are measured on the pregnant dams exposed to different levels of a toxic substance. Joint analysis of such two outcomes can result in more efficient inferences than separate models for each outcome. Most methods for joint modeling assume a normal distribution as random effects. However, in developmental toxicity studies, the response distributions may change irregularly in location and shape as the level of toxic substance changes, which may not be captured by a normal random effects model. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint model for binary and continuous outcomes. In our model, we incorporate a skewed logit model for the binary outcome to allow the response distributions to have flexibly in both symmetric and asymmetric shapes on the toxic levels. We apply our proposed method to data from a developmental toxicity study of diethylhexyl phthalate.
Keywords
Bayesian inference; diethylhexyl phthalate; joint modeling; Markov chain Monte Carlo; skewed logit model;
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