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Proportional hazard model estimation under dependent censoring using copulas and penalized likelihood
Author(s) -
Xu Jing,
Ma Jun,
Connors Michael H.,
Brodaty Henry
Publication year - 2018
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.7651
Subject(s) - censoring (clinical trials) , proportional hazards model , statistics , hazard ratio , mathematics , expectation–maximization algorithm , likelihood function , copula (linguistics) , hazard , maximum likelihood , multiplicative function , econometrics , computer science , confidence interval , mathematical analysis , chemistry , organic chemistry
This paper considers Cox proportional hazard models estimation under informative right censored data using maximum penalized likelihood, where dependence between censoring and event times are modelled by a copula function and a roughness penalty function is used to restrain the baseline hazard as a smooth function. Since the baseline hazard is nonnegative, we propose a special algorithm where each iteration involves updating regression coefficients by the Newton algorithm and baseline hazard by the multiplicative iterative algorithm. The asymptotic properties for both regression coefficients and baseline hazard estimates are developed. The simulation study investigates the performance of our method and also compares it with an existing maximum likelihood method. We apply the proposed method to a dementia patients dataset.