Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 27, Issue 1, Pages 48-58Publisher
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2017.1349665
Keywords
Cure model; Data augmentation; EM algorithm; Generalized odds rate model; Interval censoring
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Funding
- NCI NIH HHS [P01 CA142538] Funding Source: Medline
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For semiparametric survival models with interval-censored data and a cure fraction, it is often difficult to derive nonparametric maximum likelihood estimation due to the challenge in maximizing the complex likelihood function. In this article, we propose a computationally efficient EM algorithm, facilitated by a gamma-Poisson data augmentation, for maximum likelihood estimation in a class of generalized odds rate mixture cure (GORMC) models with interval-censored data. The gamma-Poisson data augmentation greatly simplifies the EM estimation and enhances the convergence speed of the EM algorithm. The empirical properties of the proposed method are examined through extensive simulation studies and compared with numerical maximum likelihood estimates. An R package GORCure is developed to implement the proposed method and its use is illustrated by an application to the Aerobic Center Longitudinal Study dataset. Supplementary material for this article is available online.
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