1. A Flexible, Computationally Efficient Method for Fitting the Proportional Hazards Model to Interval-Censored Data.
- Author
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Wang, Lianming, McMahan, Christopher S., Hudgens, Michael G., and Qureshi, Zaina P.
- Subjects
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PROPORTIONAL hazards models , *CONFIDENCE intervals , *REGRESSION analysis , *RANDOMIZED controlled trials - Abstract
The proportional hazards model (PH) is currently the most popular regression model for analyzing time-toevent data. Despite its popularity, the analysis of interval-censored data under the PH model can be challenging using many available techniques. This article presents a new method for analyzing interval-censored data under the PH model. The proposed approach uses a monotone spline representation to approximate the unknown nondecreasing cumulative baseline hazard function. Formulating the PH model in this fashion results in a finite number of parameters to estimate while maintaining substantial modeling flexibility. A novel expectation-maximization (EM) algorithm is developed for finding the maximum likelihood estimates of the parameters. The derivation of the EM algorithm relies on a two-stage data augmentation involving latent Poisson random variables. The resulting algorithm is easy to implement, robust to initialization, enjoys quick convergence, and provides closed-form variance estimates. The performance of the proposed regression methodology is evaluated through a simulation study, and is further illustrated using data from a large population-based randomized trial designed and sponsored by the United States National Cancer Institute. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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