Mixture cure semiparametric additive hazard models under partly interval censoring — a penalized likelihood approach

Survival analysis can sometimes involve individuals who will not experience the event of interest, forming what is known as the “cured group”. Identifying such individuals is not always possible beforehand, as they provide only right-censored data. Ignoring the presence of the cured group can introd...

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Vydané v:	Statistics and computing Ročník 35; číslo 4
Hlavní autori:	Li, Jinqing, Ma, Jun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.08.2025 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Censored data (mathematics) Computer Science Original Paper Probability and Statistics in Computer Science Regression coefficients Statistical Theory and Methods Statistics and Computing/Statistics Programs Interval censoring Automatic smoothing Mixture cure model Additive hazards model Maximum penalized likelihood estimation
ISSN:	0960-3174, 1573-1375
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Abstract	Survival analysis can sometimes involve individuals who will not experience the event of interest, forming what is known as the “cured group”. Identifying such individuals is not always possible beforehand, as they provide only right-censored data. Ignoring the presence of the cured group can introduce bias in the final model. This paper presents a method for estimating a semiparametric additive hazards model that accounts for the cured fraction. Unlike regression coefficients in a hazard ratio model, those in an additive hazard model measure hazard differences. The proposed method uses a primal-dual interior point algorithm to obtain constrained maximum penalized likelihood estimates of the model parameters, including the regression coefficients and the baseline hazard, subject to certain non-negativity constraints.
AbstractList	Survival analysis can sometimes involve individuals who will not experience the event of interest, forming what is known as the “cured group”. Identifying such individuals is not always possible beforehand, as they provide only right-censored data. Ignoring the presence of the cured group can introduce bias in the final model. This paper presents a method for estimating a semiparametric additive hazards model that accounts for the cured fraction. Unlike regression coefficients in a hazard ratio model, those in an additive hazard model measure hazard differences. The proposed method uses a primal-dual interior point algorithm to obtain constrained maximum penalized likelihood estimates of the model parameters, including the regression coefficients and the baseline hazard, subject to certain non-negativity constraints.
ArticleNumber	92
Author	Li, Jinqing Ma, Jun
Author_xml	– sequence: 1 givenname: Jinqing surname: Li fullname: Li, Jinqing email: actuaryljq@uibe.edu.cn organization: Department of Statistics and Actuarial Studies, School of Insurance and Economics, University of International Business and Economics – sequence: 2 givenname: Jun surname: Ma fullname: Ma, Jun organization: Depart of Mathematics and Statistics, Macquarie University
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Snippet	Survival analysis can sometimes involve individuals who will not experience the event of interest, forming what is known as the “cured group”. Identifying such...
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SubjectTerms	Algorithms Artificial Intelligence Censored data (mathematics) Computer Science Original Paper Probability and Statistics in Computer Science Regression coefficients Statistical Theory and Methods Statistics and Computing/Statistics Programs
Title	Mixture cure semiparametric additive hazard models under partly interval censoring — a penalized likelihood approach
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