title: A frailty model approach for regression analysis of multivariate current status data.
abstract: This paper discusses regression analysis of multivariate current status failure time data (The Statistical Analysis of Interval-censoring Failure Time Data. Springer: New York, 2006), which occur quite often in, for example, tumorigenicity experiments and epidemiologic investigations of the natural history of a disease. For the problem, several marginal approaches have been proposed that model each failure time of interest individually (Biometrics 2000; 56:940-943; Statist. Med. 2002; 21:3715-3726). In this paper, we present a full likelihood approach based on the proportional hazards frailty model. For estimation, an Expectation Maximization (EM) algorithm is developed and simulation studies suggest that the presented approach performs well for practical situations. The approach is applied to a set of bivariate current status data arising from a tumorigenicity experiment.
authors.affiliation: Department of Statistics, Tamkang University, Tamsui 25137, Taiwan. mchen@mail.tku.edu.tw
authors.forename: Man-Hua
authors.initials: MH
authors.lastname: Chen
authors.forename: Xingwei
authors.initials: X
authors.lastname: Tong
authors.forename: Jianguo
authors.initials: J
authors.lastname: Sun
mesh_terms: D000310:Adrenal Gland Neoplasms / Q000139:chemically induced; D000465:Algorithms; D000818:Animals; D002737:Chloroprene / Q000633:toxicity; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000139:chemically induced; D008297:Male; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D051381:Rats; D011916:Rats, Inbred F344; D012044:Regression Analysis; D016019:Survival Analysis
chemical_list: D002737:Chloroprene