A new method for regression analysis of interval-censored data with the additive hazards model

The additive hazards model is one of the most popular regression models for analyzing failure time data, especially when one is interested in the excess risk or risk difference. Although a couple of methods have been developed in the literature for regression analysis of interval-censored data, a ge...

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Bibliographic Details
Published in:Journal of the Korean Statistical Society Vol. 49; no. 4; pp. 1131 - 1147
Main Authors: Wang, Peijie, Zhou, Yong, Sun, Jianguo
Format: Journal Article
Language:English
Published: Singapore Springer Singapore 01.12.2020
한국통계학회
Subjects:
Applied Statistics
Bayesian Inference
Mathematics and Statistics
Research Article
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
통계학
Interval-censored data
EM algorithm
Latent Poisson random variable
Additive hazards model
ISSN:1226-3192, 2005-2863
Online Access:Get full text
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Summary:The additive hazards model is one of the most popular regression models for analyzing failure time data, especially when one is interested in the excess risk or risk difference. Although a couple of methods have been developed in the literature for regression analysis of interval-censored data, a general type of failure time data, they may be complicated or inefficient. Corresponding to this, we present a new maximum likelihood estimation procedure based on the sieve approach and in particular, develop an EM algorithm that involves a two-stage data augmentation with the use of Poisson latent variables. The method can be easily implemented and the asymptotic properties of the proposed estimators are established. A simulation study is conducted to assess the performance of the proposed method and indicates that it works well for practical situations. Also the method is applied to a set of interval-censored data from an AIDS cohort study.
ISSN:1226-3192
2005-2863
DOI:10.1007/s42952-020-00051-y