Inferring latent heterogeneity using many feature variables supervised by survival outcome.

ABSTRACT: In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high-risk patients at the right time. Many feature variables such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution. The Bayesian information criterion is used for selecting the number of latent groups. Furthermore, we incorporate variable selection with the adaptive lasso into inference so that only a few feature variables will be selected to characterize the latent heterogeneity. We show t

SUBMITTER: Jia B 

PROVIDER: S-EPMC8237103 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature