A Class of Joint Models for Multivariate Longitudinal Measurements and a Binary Event: An Application to a Fetal Growth Study with Longitudinal Ultrasound Measurements

• Activity Number: 474
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Sungduk Kim*+ and Paul S. Albert and Kyeongmi Cheon
• Companies: National Institutes of Health and National Institute of Child Health and Human Development and National Institute of Child Health and Human Development
• Keywords: asymmetric link ; generalized t-distribution ; macrosomia ; multivariate scale-mixture normal distributions ; prediction ; ultrasound measurement

Abstract

A large fetus is a major concern to obstetricians, affecting both maternal and fetal morbidity and mortality. Predicting large fetuses at birth has long been a challenge in obstetric practice. We use the data from a previous study conducted in Norway and Sweden from 1986 - 1989 in which each pregnant woman had four ultrasound exams. At birth, an infant is diagnosed with macrosomia if their birth weight>4,000 grams. The focus of this paper is on developing a flexible class of joint models for the multivariate longitudinal ultrasound measurements that can be used for predicting a binary event at birth. A skewed multivariate scale-mixture normal distribution is proposed for the multivariate ultrasound measurements and the skewed generalized t-link is assumed for the link function relating the binary event and the underlying longitudinal processes. We consider a shared random effect to link the two processes together. Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. The proposed methodology is motivated by and applied to a longitudinal fetal growth study.