A functional proportional hazard cure rate model for interval-censored data. (January 2022)

• Record Type: Journal Article
• Authors: Shi, Haolun; Ma, Da; Faisal Beg, Mirza; Cao, Jiguo
• Is Part Of: Statistical methods in medical research. Volume 31:Number 1(2022)
• Page Start: 154
• Page End: 168
• Publication Date: 2022-01
• Subjects: Cox's model -- functional principal component analysis -- functional data analysis -- proportional hazards model -- survival analysis -- interval-censored data; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802211052972
• Languages: English
• ISSNs: 0962-2802

Abstract

Existing survival models involving functional covariates typically rely on the Cox proportional hazards structure and the assumption of right censorship. Motivated by the aim of predicting the time of conversion to Alzheimer's disease from sparse biomarker trajectories in patients with mild cognitive impairment, we propose a functional mixture cure rate model with both functional and scalar covariates for interval censoring and sparsely sampled functional data. To estimate the nonparametric coefficient function that depicts the effect of the shape of the trajectories on the survival outcome and cure probability, we utilize the functional principal component analysis to extract the functional features from the sparsely and irregularly sampled trajectories. To obtain parameter estimates from the mixture cure rate model with interval censoring, we apply the expectation-maximization algorithm based on Poisson data augmentation. The estimation accuracy of our method is assessed via a simulation study and we apply our model on Alzheimer's disease Neuroimaging Initiative data set.