@article{0db53ee325b84251b408998a8286d663,
title = "Censored quantile regression survival models with a cure proportion",
abstract = "A new quantile regression model for survival data is proposed that permits a positive proportion of subjects to become unsusceptible to recurrence of disease following treatment or based on other observable characteristics. In contrast to prior proposals for quantile regression estimation of censored survival models, we propose a new “data augmentation” approach to estimation. Our approach has computational advantages over earlier approaches proposed by Wu and Yin (2013, 2017). We compare our method with the two estimation strategies proposed by Wu and Yin and demonstrate its advantageous empirical performance in simulations. The methods are also illustrated with data from a Lung Cancer survival study.",
keywords = "Cure proportion, Data augmentation, Mixture models, Quantile regression, Survival data",
author = "Naveen Narisetty and Roger Koenker",
note = "The initial phase of this research was partially supported by Bristol-Myers Squibb, USA. Both authors would like to acknowledge valuable conversations with Xuming He about the subject matter addressed. The first author would like to acknowledge partial support from NSF grants DMS-1811768 and CAREER-1943500. The second author would like to express his appreciation to Gary Chamberlain for his encouragement and inspiration over several decades, beginning with his two early JRSS(B) papers, Chamberlain and Leamer (1976) and Leamer and Chamberlain (1976), that introduced data augmentation in a remarkably general setting to econometrics. The initial phase of this research was partially supported by Bristol-Myers Squibb, USA . Both authors would like to acknowledge valuable conversations with Xuming He about the subject matter addressed. The second author would like to express his appreciation to Gary Chamberlain for his encouragement and inspiration over several decades, beginning with his two early JRSS(B) papers, Chamberlain and Leamer (1976) and Leamer and Chamberlain (1976) , that introduced data augmentation in a remarkably general setting to econometrics.",
year = "2022",
month = jan,
doi = "10.1016/j.jeconom.2020.12.005",
language = "English (US)",
volume = "226",
pages = "192--203",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier B.V.",
number = "1",
}