Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information.

ABSTRACT: We aim to construct more accurate prognostic model for KIRC patients by combining the clinical and genetic information and monitor the disease progression in dynamically updated manner. By obtaining cross-validated prognostic indices from clinical and genetic model, we combine the two sources information into the Super learner model, and then introduce the time-varying effect into the combined model using the landmark method for real-time dynamic prediction. The Super learner model has better prognostic performance since it can not only employ the preferable clinical prognostic model constructed by oneself or reported in the current literature, but also incorporate genome level information to strengthen effectiveness. Apart from this, four representative patients' mortality curves are draw

SUBMITTER: Zhao H 

PROVIDER: S-EPMC6279814 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature