|Title||An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Zhou, Jie, Jiajia Zhang, and Wenbin Lu|
|Date Published||2017 Mar 30|
|Keywords||Algorithms, Breast Neoplasms, Data Interpretation, Statistical, Female, Hemophilia A, HIV Infections, Humans, Likelihood Functions, Models, Statistical, Odds Ratio, Poisson Distribution, Proportional Hazards Models|
The generalized odds-rate model is a class of semiparametric regression models, which includes the proportional hazards and proportional odds models as special cases. There are few works on estimation of the generalized odds-rate model with interval censored data because of the challenges in maximizing the complex likelihood function. In this paper, we propose a gamma-Poisson data augmentation approach to develop an Expectation Maximization algorithm, which can be used to fit the generalized odds-rate model to interval censored data. The proposed Expectation Maximization algorithm is easy to implement and is computationally efficient. The performance of the proposed method is evaluated by comprehensive simulation studies and illustrated through applications to datasets from breast cancer and hemophilia studies. In order to make the proposed method easy to use in practice, an R package 'ICGOR' was developed. Copyright © 2016 John Wiley & Sons, Ltd.
|Alternate Journal||Stat Med|
|Original Publication||An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.|
|PubMed Central ID||PMC5998339|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.