A flexible, computationally efficient method for fitting the proportional hazards model to interval-censored data.

ABSTRACT: The proportional hazards model (PH) is currently the most popular regression model for analyzing time-to-event data. Despite its popularity, the analysis of interval-censored data under the PH model can be challenging using many available techniques. This article presents a new method for analyzing interval-censored data under the PH model. The proposed approach uses a monotone spline representation to approximate the unknown nondecreasing cumulative baseline hazard function. Formulating the PH model in this fashion results in a finite number of parameters to estimate while maintaining substantial modeling flexibility. A novel expectation-maximization (EM) algorithm is developed for finding the maximum likelihood estimates of the parameters. The derivation of the EM algorithm relies on a t

SUBMITTER: Wang L 

PROVIDER: S-EPMC4803641 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature