Modified score function for monotone likelihood in the semiparametric mixture cure model.
Cox regression
cure rate
em algorithm
firth method
melanoma
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
03 2022
03 2022
Historique:
revised:
06
04
2021
received:
22
08
2020
accepted:
01
05
2021
pubmed:
1
12
2021
medline:
26
4
2022
entrez:
30
11
2021
Statut:
ppublish
Résumé
The cure fraction models are intended to analyze lifetime data from populations where some individuals are immune to the event under study, and allow a joint estimation of the distribution related to the cured and susceptible subjects, as opposed to the usual approach ignoring the cure rate. In situations involving small sample sizes with many censored times, the detection of nonfinite coefficients may arise via maximum likelihood. This phenomenon is commonly known as monotone likelihood (ML), occurring in the Cox and logistic regression models when many categorical and unbalanced covariates are present. An existing solution to prevent the issue is based on the Firth correction, originally developed to reduce the estimation bias. The method ensures finite estimates by penalizing the likelihood function. In the context of mixture cure models, the ML issue is rarely discussed in the literature; therefore, this topic can be seen as the first contribution of our paper. The second major contribution, not well addressed elsewhere, is the study of the ML issue in cure mixture modeling under the flexibility of a semiparametric framework to handle the baseline hazard. We derive the modified score function based on the Firth approach and explore finite sample size properties of the estimators via a Monte Carlo scheme. The simulation results indicate that the performance of coefficients related to the binary covariates are strongly affected to the imbalance degree. A real illustration, in the melanoma dataset, is discussed using a relatively novel data set collected in a Brazilian university hospital.
Identifiants
pubmed: 34845768
doi: 10.1002/bimj.202000254
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
635-654Informations de copyright
© 2021 Wiley-VCH GmbH.
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