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A flexible, computationally efficient method for fitting the proportional hazards
model to interval-censored data
#MMPMID26393917
Wang L
; McMahan CS
; Hudgens MG
; Qureshi ZP
Biometrics
2016[Mar]; 72
(1
): 222-31
PMID26393917
show ga
The proportional hazards model (PH) is currently the most popular regression
model for analyzing time-to-event 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.
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