Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models.

ABSTRACT: Analyzing the large-scale survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program may help guide the management of cancer. Detecting and characterizing the time-varying effects of factors collected at the time of diagnosis could reveal important and useful patterns. However, fitting a time-varying effect model by maximizing the partial likelihood with such large-scale survival data is not feasible with most existing software. Moreover, estimating time-varying coefficients using spline based approaches requires a moderate number of knots, which may lead to unstable estimation and over-fitting issues. To resolve these issues, adding a penalty term greatly aids estimation. The selection of penalty smoothing parameters is difficult in this

SUBMITTER: Luo L 

PROVIDER: S-EPMC10868332 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature