An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.

TitleAn Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.
Publication TypeJournal Article
Year of Publication2017
AuthorsZhou, Jie, Jiajia Zhang, and Wenbin Lu
JournalStat Med
Volume36
Issue7
Pagination1157-1171
Date Published2017 03 30
ISSN1097-0258
KeywordsAlgorithms, Breast Neoplasms, Data Interpretation, Statistical, Female, Hemophilia A, HIV Infections, Humans, Likelihood Functions, Models, Statistical, Odds Ratio, Poisson Distribution, Proportional Hazards Models
Abstract

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.

DOI10.1002/sim.7204
Alternate JournalStat Med
Original PublicationAn Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.
PubMed ID28004414
PubMed Central IDPMC5998339
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
Project: