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Activity Number: 426 - SPEED: Biopharmaceutical and General Health Studies: Statistical Methods and Applications, Part 2
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 3:05 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #307838
Title: Bayesian Semiparametric Joint Modeling of Longitudinal Predictors and a Binary Outcome
Author(s): Woobeen Lim* and Michael Pennell
Companies: The Ohio State University and Ohio State University
Keywords: joint model; Longitudinal measurement; mixed-effects model; Dirichlet Process; spline regression; semiparametric

Many prospective studies in biomedical areas collect data on longitudinal variables that are predictive of a binary outcome. Examples include a study predicting successful pregnancy of women with treatment for infertility based on longitudinal responses of adhesiveness of certain blood lymphocytes and a study of bone mineral density in women transitioning to menopause. Common problems in these examples are inconsistency in timing of measurements and missing follow-ups that complicate analysis. Motivated by a cancer survivor cohort study, a new class of joint models for a binary outcome and longitudinal predictors of different scale are proposed as the solution for these challenges. The longitudinal model uses a latent normal random variable construction with regression splines to model time-dependent trends in mean with a Dirichlet Process prior assigned to random effects to decrease distribution assumptions. Also, a binary outcome is related to augmented predictor values at a set time point, thereby standardizing timing of predictors.

Authors who are presenting talks have a * after their name.

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