|Title||Computationally Efficient Estimation for the Generalized Odds Rate Mixture Cure Model with Interval-Censored Data.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Zhou, Jie, Jiajia Zhang, and Wenbin Lu|
|Journal||J Comput Graph Stat|
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 paper, 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.
|Alternate Journal||J Comput Graph Stat|
|Original Publication||Computationally efficient estimation for the generalized odds rate mixture cure model with interval censored data|
|PubMed Central ID||PMC5978779|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
Computationally Efficient Estimation for the Generalized Odds Rate Mixture Cure Model with Interval-Censored Data.