Time-varying Hazards Model for Incorporating Irregularly Measured, High-Dimensional Biomarkers.

ABSTRACT: Clinical studies with time-to-event outcomes often collect measurements of a large number of time-varying covariates over time (e.g., clinical assessments or neuroimaging biomarkers) to build time-sensitive prognostic model. An emerging challenge is that due to resource-intensive or invasive (e.g., lumbar puncture) data collection process, biomarkers may be measured infrequently and thus not available at every observed event time point. Lever-aging all available, infrequently measured time-varying biomarkers to improve prognostic model of event occurrence is an important and challenging problem. In this paper, we propose a kernel-smoothing based approach to borrow information across subjects to remedy infrequent and unbalanced biomarker measurements under a time-varying hazards model. A pe

SUBMITTER: Li X 

PROVIDER: S-EPMC7497773 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature