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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-025-10622-w