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

Jinqing Li, Jun Ma

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)

    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.
    Original languageEnglish
    Pages (from-to)170-180
    Number of pages11
    JournalComputational Statistics and Data Analysis
    Volume137
    Early online date28 Feb 2019
    DOIs
    Publication statusPublished - Sep 2019

    Keywords

    • Additive hazards model
    • Interval censoring
    • Maximum penalized likelihood estimation
    • Primal–dual interior point algorithm
    • Automatic smoothing value selection

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