Bayesian Regression for High Dimensional Short-Term Longitudinal Data
Abstract Number:
2956
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Livia Popa (1), Sumanta Basu (2), Martin Wells (2), Myung Hee Lee (3)
Institutions:
(1) Cornell University, Ithaca, New York, (2) Cornell University, N/A, (3) Weill Cornell Medicine, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Clinicians are increasingly interested in discovering computational biomarkers from short-term longitudinal 'omics data sets. Existing methods in the high-dimensional setting use penalized regression and do not offer uncertainty quantification. This work focuses on Bayesian high-dimensional regression and variable selection for longitudinal 'omics datasets, which can quantify uncertainty and control for false discovery.
We adopt both empirical Bayes as well as hierarchical Bayes principles for hyperparameter selection. Our Bayesian methods use a Markov Chain Monte Carlo (MCMC) approach and a novel Expectation Maximization (EM) algorithm for posterior inference. We conduct extensive numerical experiments on simulated data to compare our method against existing frequentist alternatives. We also illustrate our method on a pulmonary tuberculosis (TB) study consisting of 4-time point observations for 15 subjects, each with measured sputum mycobacterial load.
Keywords:
Disease Progression|EM algorithm|Feature selection|Mixed Models|Mixture model|Uncertainty Quantification
Sponsors:
Biometrics Section
Tracks:
High Dimensional Regression
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