Proportional hazard model estimation under dependent censoring using copulas and penalized likelihood

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...

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Bibliographic Details
Published in:Statistics in medicine 2018-06, Vol.37 (14), p.2238-2251
Main Authors: Xu, Jing, Ma, Jun, Connors, Michael H., Brodaty, Henry
Format: Article
Language:English
Subjects:
Algorithms
Computer Simulation
constrained optimization
copulas
Dementia
dependent censoring
Economic models
Humans
Likelihood Functions
Maximum likelihood method
maximum penalized likelihood
Proportional Hazards Models
sensitivity analysis
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Summary: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.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.7651