Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients.

ABSTRACT: Cohort studies often provide a large array of data on study participants. The techniques of statistical learning can allow an efficient way to analyze large datasets in order to uncover previously unknown, clinically relevant predictors of morbidity or mortality. We applied a combination of elastic net penalized Cox regression and stability selection with the aim of identifying novel predictors of mortality in a cohort of prevalent hemodialysis patients. In our analysis we included 475 patients from the "rISk strAtification in end-stage Renal disease" (ISAR) study, who we split into derivation and confirmation cohorts. A wide array of examinations was available for study participants, resulting in over a hundred potential predictors. In the selection approach many of the well established p

SUBMITTER: Werfel S 

PROVIDER: S-EPMC8085040 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature