A class of linear transformation models for interval censored data with a cured proportion. Issue 10 (26th November 2017)

• Record Type: Journal Article
• Authors: Chen, Man-Hua; Yang, Hsin-Yu
• Is Part Of: Communications in statistics. Volume 46:Issue 10(2017)
• Page Start: 8251
• Page End: 8263
• Publication Date: 2017-11-26
• Subjects: Cured subgroup -- EM algorithm -- Interval-censoring -- Linear transformation models -- Logistic regression; 62N01; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1277744
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Interval-censored data arise due to a sequence random examination such that the failure time of interest occurs in an interval. In some medical studies, there exist long-term survivors who can be considered as permanently cured. We consider a mixed model for the uncured group coming from linear transformation models and cured group coming from a logistic regression model. For the inference of parameters, an EM algorithm is developed for a full likelihood approach. To investigate finite sample properties of the proposed method, simulation studies are conducted. The approach is applied to the National Aeronautics and Space Administration's hypobaric decompression sickness data.