Cox regression models with functional covariates for survival data. (June 2015)

• Record Type: Journal Article
• Authors: Gellar, Jonathan E.; Colantuoni, Elizabeth; Needham, Dale M.; Crainiceanu, Ciprian M.
• Is Part Of: Statistical modelling. Volume 15:Number 3(2015)
• Page Start: 256
• Page End: 278
• Publication Date: 2015-06
• Subjects: functional data analysis -- Survival analysis -- Cox proportional hazards model -- nonparametric statistics -- intensive care unit; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X14565526
• Languages: English
• ISSNs: 1471-082X

Abstract

We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.