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A semiparametric probit model for case 2 interval‐censored failure time data
Author(s) -
Lin Xiaoyan,
Wang Lianming
Publication year - 2010
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.3832
Subject(s) - probit model , probit , semiparametric regression , interval (graph theory) , accelerated failure time model , semiparametric model , econometrics , nonparametric statistics , computer science , monotone polygon , statistics , bayesian probability , survival function , feature (linguistics) , mathematics , survival analysis , linguistics , philosophy , geometry , combinatorics
Abstract Interval‐censored data occur naturally in many fields and the main feature is that the failure time of interest is not observed exactly, but is known to fall within some interval. In this paper, we propose a semiparametric probit model for analyzing case 2 interval‐censored data as an alternative to the existing semiparametric models in the literature. Specifically, we propose to approximate the unknown nonparametric nondecreasing function in the probit model with a linear combination of monotone splines, leading to only a finite number of parameters to estimate. Both the maximum likelihood and the Bayesian estimation methods are proposed. For each method, regression parameters and the baseline survival function are estimated jointly. The proposed methods make no assumptions about the observation process and can be applicable to any interval‐censored data with easy implementation. The methods are evaluated by simulation studies and are illustrated by two real‐life interval‐censored data applications. Copyright © 2010 John Wiley & Sons, Ltd.