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March 2017 A penalized Cox proportional hazards model with multiple time-varying exposures
Chenkun Wang, Hai Liu, Sujuan Gao
Ann. Appl. Stat. 11(1): 185-201 (March 2017). DOI: 10.1214/16-AOAS999


In recent pharmacoepidemiology research, the increasing use of electronic medication dispensing data provides an unprecedented opportunity to examine various health outcomes associated with long-term medication usage. Often, patients may take multiple types of medications intended for the same medical condition and the medication exposure status and intensity may vary over time, posing challenges to the statistical modeling of such data. In this article, we propose a penalized Cox proportional hazards (PH) model with multiple functional covariates and potential interaction effects. We also consider constrained coefficient functions to ensure a diminishing medication effect over time. Hypothesis testing of interaction effect and main effect was discussed under the penalized Cox PH model setting. Our simulation studies demonstrate the adequate performance of the proposed methods for both parameter estimation and hypothesis testing. Application to a primary care depression cohort study was also illustrated to examine the effects of two common types of antidepressants on the risk of coronary artery disease.


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Chenkun Wang. Hai Liu. Sujuan Gao. "A penalized Cox proportional hazards model with multiple time-varying exposures." Ann. Appl. Stat. 11 (1) 185 - 201, March 2017.


Received: 1 May 2015; Revised: 1 October 2016; Published: March 2017
First available in Project Euclid: 8 April 2017

zbMATH: 1366.62245
MathSciNet: MR3634320
Digital Object Identifier: 10.1214/16-AOAS999

Rights: Copyright © 2017 Institute of Mathematical Statistics


Vol.11 • No. 1 • March 2017
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