FLCRM: Functional Linear Cox Regression Model

2017/09/01 by Dehan Kong, Joseph G. Ibrahim, Eunjee Lee +1 · 92 citations
Computer Science · Mathematics · #Advanced Statistical Methods and Models #Bayesian Methods and Mixture Models #Bayesian multivariate linear regression #Computer science #Linear model #Linear regression #Mathematics #Proper linear model #Proportional hazards model #Regression #Regression analysis #Statistical Methods and Inference #Statistics

paper · open access · doi:10.1111/biom.12748

published in Biometrics 74(1), 109-117 (Oxford University Press)

openalex publication_date 2017/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

We consider a functional linear Cox regression model for characterizing the association between time-to-event data and a set of functional and scalar predictors. The functional linear Cox regression model incorporates a functional principal component analysis for modeling the functional predictors and a high-dimensional Cox regression model to characterize the joint effects of both functional and scalar predictors on the time-to-event data. We develop an algorithm to calculate the maximum approximate partial likelihood estimates of unknown finite and infinite dimensional parameters. We also systematically investigate the rate of convergence of the maximum approximate partial likelihood estimates and a score test statistic for testing the nullity of the slope function associated with the functional predictors. We demonstrate our estimation and testing procedures by using simulations and the analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Our real data analyses show that high-dimensional hippocampus surface data may be an important marker for predicting time to conversion to Alzheimer's disease. Data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu).

Citations

• Proportional hazards tests and diagnostics based on weighted residuals

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