Personalized risk prediction using longitudinal data: Applications in obstetrics and cancer
Friday, November 9, 2018 - 9:00am - 9:50am
Assessing disease risk with repeated biomarker measurements has important applications in the screening and early detection for many diseases. In obstetrics, interest is on assessing the risk of a poor pregnancy outcome (i.e., small for gestational age or preterm birth) from longitudinal imaging and biomarker data. We will begin by presenting simple two-stage estimation procedure that approximates a full maximum-likelihood approach for predicting a binary event from multivariate longitudinal growth data using a shared random parameter model (Albert, Statistics in Medicine, 2012). We will present a pattern mixture approximation to this model (Liu and Albert, Biostatistics, 2014). Subsequently, we will present a class of joint models for multivariate growth curve data and a binary event that accommodates a flexible skewed error distribution for the ultrasound measurements and an asymmetric link function relating the longitudinal to the binary process (Kim and Albert, Biometrics, 2016). We will also present a tree-based approach for identifying subgroups of women who have an enhanced predictive accuracy for predicting a binary event from fetal growth data (Foster, et al., JRSS-A, 2016). Finally, we will discuss applications of these methods for early detection of cancer.