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Activity Number: 339 - SPEED: Biopharmaceutical and General Health Studies: Statistical Methods and Applications, Part 1
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #303025 Presentation 1 Presentation 2 Presentation 3 Presentation 4 Presentation 5 Presentation 6
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
Abstract:

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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