Regression analysis of current status data in the presence of a cured subgroup and dependent censoring

• Published in: Lifetime data analysis Vol. 23; no. 4; pp. 626 - 650
• Main Authors: Liu, Yeqian, Hu, Tao, Sun, Jianguo
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2017; Springer Nature B.V
• Subjects: Algorithms; Animals; Asymptotic properties; Chloroprene - toxicity; Computer Simulation; Economic models; Economics; Estimators; Failure analysis; Female; Finance; Health Sciences; Humans; Insurance; Life Tables; Likelihood Functions; Liver Neoplasms, Experimental - chemically induced; Male; Management; Mathematics and Statistics; Maximum likelihood estimation; Medicine; Mice; Models, Statistical; Operations Research/Decision Theory; Polynomials; Quality Control; Rats; Regression Analysis; Reliability; Safety and Risk; Statistics; Statistics for Business; Statistics for Life Sciences; Interval censoring; Cure rate model; EM algorithm; Bernstein polynomial
• ISSN: 1380-7870; 1572-9249; 1572-9249
• DOI: 10.1007/s10985-016-9382-z

This paper discusses regression analysis of current status data, a type of failure time data where each study subject is observed only once, in the presence of dependent censoring. Furthermore, there may exist a cured subgroup, meaning that a proportion of study subjects are not susceptible to the failure event of interest. For the problem, we develop a sieve maximum likelihood estimation approach with the use of latent variables and Bernstein polynomials. For the determination of the proposed estimators, an EM algorithm is developed and the asymptotic properties of the estimators are established. Extensive simulation studies are conducted and indicate that the proposed method works well for practical situations. A motivating application from a tumorigenicity experiment is also provided.