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