@article{988d2d66287a44d190b8204ec904c7b4,
title = "Semiparametric maximum likelihood inference for truncated or biased-sampling data",
abstract = "Sample selection bias has long been recognized in many fields including clinical trials, epidemiology studies, genome-wide association studies, and wildlife management. This paper investigates the maximum likelihood estimation for censored survival data with selection bias under the Cox regression models where the selection process is modeled parametrically. A novel expectation-maximization algorithm is proposed and shown to have considerable computational advantages. Rigorous asymptotic properties of the estimator are established. Extensive simulation studies and a data analysis are conducted to investigate the performance of the proposed estimation procedure.",
keywords = "Biased sampling, Length bias data, Truncated and rightcensored survival data",
author = "Hao Liu and Jing Ning and Jing Qin and Yu Shen",
note = "Funding Information: We thank Professor Masoud Asgharian and the investigators of the Canadian Study of Health and Aging (CHSA) for providing us with the dementia data, which were collected as part of the CHSA, funded by the Seniors' Independence Research Program, through the National Health Research and Development Program of Health Canada (Project no.6606-3954-MC(S)). Additional funding was provided by Pfizer Canada Incorporated through the Medical Research Council/ Pharmaceutical Manufacturers Association of Canada Health Activity Program, NHRDP Project 6603-1417-302(R), Bayer Incorporated, and the British Columbia Health Research Foundation Projects 38 (93-2) and 34 (96-1). The study was coordinated through the University of Ottawa and the Division of Aging and Seniors, Health Canada. This work in this paper was supported in part by the National Institutes of Health",
year = "2016",
month = jul,
doi = "10.5705/ss.2014.094",
language = "English (US)",
volume = "26",
pages = "1087--1115",
journal = "Statistica Sinica",
issn = "1017-0405",
publisher = "Institute of Statistical Science",
number = "3",
}