Abstract
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.
Original language | English |
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Pages (from-to) | 2238-2251 |
Number of pages | 14 |
Journal | Statistics in Medicine |
Volume | 37 |
Issue number | 14 |
DOIs | |
Publication status | Published - 30 Jun 2018 |
Keywords
- constrained optimization
- copulas
- dependent censoring
- maximum penalized likelihood
- sensitivity analysis