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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Vydáno v:Statistics and computing Ročník 35; číslo 4
Hlavní autoři: Li, Jinqing, Ma, Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.08.2025
Springer Nature B.V
Témata:
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
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  givenname: Jun
  surname: Ma
  fullname: Ma, Jun
  organization: Depart of Mathematics and Statistics, Macquarie University
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Cites_doi 10.1111/j.0006-341X.2000.00227.x
10.2307/2529885
10.1016/j.csda.2019.02.010
10.1111/j.2517-6161.1982.tb01203.x
10.1016/j.csda.2021.107365
10.1111/1467-9574.00175
10.1017/CBO9780511755453
10.1002/sim.7651
10.1137/1.9781611971453
10.1109/TSP.2007.907814
10.1007/978-1-4899-4473-3
10.1093/biomet/81.1.61
10.4310/SII.2015.v8.n3.a10
10.1214/aos/1176345782
10.1007/BF03178906
10.1111/j.1467-9868.2007.00589.x
10.1007/s10985-021-09515-7
10.1198/016214501753382273
10.1198/jasa.2009.tm08033
10.1002/sim.4780080803
10.1111/1467-9868.00398
10.1080/00949655.2021.1880587
10.1198/016214502388618861
10.1002/sim.9415
10.1214/aos/1015362185
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Automatic smoothing
Mixture cure model
Additive hazards model
Maximum penalized likelihood estimation
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References 10622_CR8
V Farewell (10622_CR5) 1982; 38
10622_CR9
OO Aalen (10622_CR1) 1989; 8
S Geman (10622_CR6) 1982; 10
JS Kim (10622_CR10) 2003; 65
DY Lin (10622_CR13) 1994; 81
J Fan (10622_CR4) 2002; 30
J Ma (10622_CR15) 2021; 18
M Othus (10622_CR17) 2009; 104
10622_CR19
T Moore (10622_CR16) 2008; 56
J Fan (10622_CR3) 2001; 96
D Ghosh (10622_CR7) 2001; 55
J Li (10622_CR11) 2019; 137
S Wang (10622_CR22) 2021; 27
Y Yu (10622_CR27) 2002; 97
S Zhao (10622_CR28) 2015; 8
T Louis (10622_CR14) 1982; 44
J Fan (10622_CR2) 1997; 6
A Webb (10622_CR24) 2022; 41
10622_CR21
10622_CR20
S Wang (10622_CR23) 2021; 91
J Xu (10622_CR26) 2018; 37
Y Li (10622_CR12) 2007; 69
Y Qi (10622_CR18) 2022
10622_CR25
References_xml – ident: 10622_CR21
  doi: 10.1111/j.0006-341X.2000.00227.x
– volume: 38
  start-page: 1041
  year: 1982
  ident: 10622_CR5
  publication-title: Biometrics
  doi: 10.2307/2529885
– volume: 137
  start-page: 170
  year: 2019
  ident: 10622_CR11
  publication-title: Comput. Stat. Data Anal.
  doi: 10.1016/j.csda.2019.02.010
– volume: 44
  start-page: 226
  year: 1982
  ident: 10622_CR14
  publication-title: J R Stat. Soc. B
  doi: 10.1111/j.2517-6161.1982.tb01203.x
– year: 2022
  ident: 10622_CR18
  publication-title: Comput. Stat. Data Anal.
  doi: 10.1016/j.csda.2021.107365
– volume: 55
  start-page: 367
  issue: 3
  year: 2001
  ident: 10622_CR7
  publication-title: Stat. Neerlandica
  doi: 10.1111/1467-9574.00175
– ident: 10622_CR20
– ident: 10622_CR19
  doi: 10.1017/CBO9780511755453
– volume: 37
  start-page: 2238
  year: 2018
  ident: 10622_CR26
  publication-title: Stat. Med.
  doi: 10.1002/sim.7651
– ident: 10622_CR25
  doi: 10.1137/1.9781611971453
– volume: 56
  start-page: 895
  year: 2008
  ident: 10622_CR16
  publication-title: IEEE Trans. Sig. Proc
  doi: 10.1109/TSP.2007.907814
– ident: 10622_CR8
  doi: 10.1007/978-1-4899-4473-3
– volume: 81
  start-page: 61
  year: 1994
  ident: 10622_CR13
  publication-title: Biometrika
  doi: 10.1093/biomet/81.1.61
– volume: 8
  start-page: 367
  year: 2015
  ident: 10622_CR28
  publication-title: Stat. Interface.
  doi: 10.4310/SII.2015.v8.n3.a10
– volume: 10
  start-page: 401
  year: 1982
  ident: 10622_CR6
  publication-title: Annals Stat.
  doi: 10.1214/aos/1176345782
– volume: 6
  start-page: 131
  year: 1997
  ident: 10622_CR2
  publication-title: Antoniadis. J. Italian Stat. Assoc.
  doi: 10.1007/BF03178906
– volume: 69
  start-page: 285
  issue: 3
  year: 2007
  ident: 10622_CR12
  publication-title: J. Royal Stat. Soc.: Ser. B (Stat. Meth.)
  doi: 10.1111/j.1467-9868.2007.00589.x
– volume: 27
  start-page: 244
  year: 2021
  ident: 10622_CR22
  publication-title: Lifetime Data Anal.
  doi: 10.1007/s10985-021-09515-7
– volume: 96
  start-page: 1348
  year: 2001
  ident: 10622_CR3
  publication-title: J. Amer. Statist. Assoc
  doi: 10.1198/016214501753382273
– volume: 18
  start-page: 53
  issue: 2
  year: 2021
  ident: 10622_CR15
  publication-title: Int. J. Biostat.
– volume: 104
  start-page: 1241
  issue: 487
  year: 2009
  ident: 10622_CR17
  publication-title: J. Am. Stat. Assoc.
  doi: 10.1198/jasa.2009.tm08033
– volume: 8
  start-page: 907
  year: 1989
  ident: 10622_CR1
  publication-title: Stat. Med.
  doi: 10.1002/sim.4780080803
– volume: 65
  start-page: 489
  issue: 2
  year: 2003
  ident: 10622_CR10
  publication-title: J. Royal Stat. Soc.: Ser. B (Stat. Methodol.)
  doi: 10.1111/1467-9868.00398
– ident: 10622_CR9
– volume: 91
  start-page: 2018
  year: 2021
  ident: 10622_CR23
  publication-title: J. Stat. Comput. Simulation
  doi: 10.1080/00949655.2021.1880587
– volume: 97
  start-page: 1042
  year: 2002
  ident: 10622_CR27
  publication-title: J. Am. Statist. Assoc.
  doi: 10.1198/016214502388618861
– volume: 41
  start-page: 3260
  year: 2022
  ident: 10622_CR24
  publication-title: Stat. Med.
  doi: 10.1002/sim.9415
– volume: 30
  start-page: 74
  issue: 1
  year: 2002
  ident: 10622_CR4
  publication-title: Annals Stati.
  doi: 10.1214/aos/1015362185
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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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