Citation

Lin X, Wang L. Stat. Med. 2010; 29(9): 972-981.

Affiliation

Department of Statistics, University of South Carolina, 1523 Greene Street, Columbia, SC 29208, U.S.A.

Copyright

(Copyright © 2010, John Wiley and Sons)

DOI

10.1002/sim.3832

PMID

20069532

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.

Language: en