Generalized Linear Models for Flexible Parametric Modeling of the Hazard Function.

ABSTRACT: Background. Parametric modeling of survival data is important, and reimbursement decisions may depend on the selected distribution. Accurate predictions require sufficiently flexible models to describe adequately the temporal evolution of the hazard function. A rich class of models is available among the framework of generalized linear models (GLMs) and its extensions, but these models are rarely applied to survival data. This article describes the theoretical properties of these more flexible models and compares their performance to standard survival models in a reproducible case study. Methods. We describe how survival data may be analyzed with GLMs and their extensions: fractional polynomials, spline models, generalized additive models, generalized linear mixed (frailty) m

SUBMITTER: Kearns B 

PROVIDER: S-EPMC6843612 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature