Premium
The proportional odds model for multivariate interval‐censored failure time data
Author(s) -
Chen ManHua,
Tong Xingwei,
Sun Jianguo
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2907
Subject(s) - covariate , bivariate analysis , statistics , odds , multivariate statistics , censoring (clinical trials) , univariate , confidence interval , inference , odds ratio , data set , econometrics , logistic regression , computer science , mathematics , artificial intelligence
The proportional odds model is one of the most commonly used regression models in failure time data analysis and has been discussed by many authors ( Appl. Stat. 1983; 32 :165–171; J. Am. Stat. Assoc. 1999; 94 :125–136; J. Am. Stat. Assoc. 1997; 92 :960–967; Biometrics 2000; 56 :511–518; J. Am. Stat. Assoc. 2001; 96 :1446–1457). It specifies that covariates have multiplicative effects on the odds function and is often used when, for example, the covariate effect diminishes over time. Most of the existing methods for the model are for univariate failure time data. In this paper, we discuss how to fit the proportional odds model to multivariate interval‐censored failure time data. For inference, the maximum likelihood approach is developed and evaluated by simulation studies, which suggest that the method works well for practical situations. The method is applied to a set of bivariate interval‐censored data arising from an AIDS clinical trial. Copyright © 2007 John Wiley & Sons, Ltd.