Maximum penalized likelihood estimation of additive hazards models with partly interval censoring

Jinqing Li and Jun Ma

Computational Statistics & Data Analysis, 2019, vol. 137, issue C, 170-180

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.

Keywords: Additive hazards model; Interval censoring; Maximum penalized likelihood estimation; Primal–dual interior point algorithm; Automatic smoothing value selection (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:137:y:2019:i:c:p:170-180

DOI: 10.1016/j.csda.2019.02.010

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