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- W2920346585 abstract "Existing likelihood methods for the additive hazards model with interval censored survival data are limited and often ignore the non-negative constraints on hazards. This paper proposes a maximum penalized likelihood method to fit additive hazards models with interval censoring. Our method firstly models the baseline hazard using a finite number of non-negative basis functions, and then regression coefficients and baseline hazard are estimated simultaneously by maximizing a penalized log-likelihood function, where a penalty function is introduced to regularize the baseline hazard estimate. In the estimation procedure, non-negative constraints are imposed on both the baseline hazard and the hazard of each subject. A primal–dual interior-point algorithm is applied to solve the constrained optimization problem. Asymptotic properties are obtained and a simulation study is conducted for assessment of the proposed method." @default.
- W2920346585 created "2019-03-11" @default.
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- W2920346585 date "2019-09-01" @default.
- W2920346585 modified "2023-10-09" @default.
- W2920346585 title "Maximum penalized likelihood estimation of additive hazards models with partly interval censoring" @default.
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- W2920346585 doi "https://doi.org/10.1016/j.csda.2019.02.010" @default.
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