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

Jinqing Li, Jun Ma

Research output: Contribution to journalArticle

1 Citation (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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