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Generalized parametric cure models for relative survival.
Biom J. 2020 Jan 20 [Online ahead of print]BJ

Abstract

Cure models are used in time-to-event analysis when not all individuals are expected to experience the event of interest, or when the survival of the considered individuals reaches the same level as the general population. These scenarios correspond to a plateau in the survival and relative survival function, respectively. The main parameters of interest in cure models are the proportion of individuals who are cured, termed the cure proportion, and the survival function of the uncured individuals. Although numerous cure models have been proposed in the statistical literature, there is no consensus on how to formulate these. We introduce a general parametric formulation of mixture cure models and a new class of cure models, termed latent cure models, together with a general estimation framework and software, which enable fitting of a wide range of different models. Through simulations, we assess the statistical properties of the models with respect to the cure proportion and the survival of the uncured individuals. Finally, we illustrate the models using survival data on colon cancer, which typically display a plateau in the relative survival. As demonstrated in the simulations, mixture cure models which are not guaranteed to be constant after a finite time point, tend to produce accurate estimates of the cure proportion and the survival of the uncured. However, these models are very unstable in certain cases due to identifiability issues, whereas LC models generally provide stable results at the price of more biased estimates.

Authors+Show Affiliations

Department of Clinical Medicine, Aalborg University, Aalborg, Denmark. Department of Hematology, Aalborg University Hospital, Aalborg, Denmark.Department of Clinical Medicine, Aalborg University, Aalborg, Denmark. Department of Hematology, Aalborg University Hospital, Aalborg, Denmark.Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31957910

Citation

Jakobsen, Lasse Hjort, et al. "Generalized Parametric Cure Models for Relative Survival." Biometrical Journal. Biometrische Zeitschrift, 2020.
Jakobsen LH, Bøgsted M, Clements M. Generalized parametric cure models for relative survival. Biom J. 2020.
Jakobsen, L. H., Bøgsted, M., & Clements, M. (2020). Generalized parametric cure models for relative survival. Biometrical Journal. Biometrische Zeitschrift. https://doi.org/10.1002/bimj.201900056
Jakobsen LH, Bøgsted M, Clements M. Generalized Parametric Cure Models for Relative Survival. Biom J. 2020 Jan 20; PubMed PMID: 31957910.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Generalized parametric cure models for relative survival. AU - Jakobsen,Lasse Hjort, AU - Bøgsted,Martin, AU - Clements,Mark, Y1 - 2020/01/20/ PY - 2019/01/25/received PY - 2019/08/19/revised PY - 2019/08/30/accepted PY - 2020/1/21/entrez KW - cure models KW - parametric models KW - relative survival KW - splines JF - Biometrical journal. Biometrische Zeitschrift JO - Biom J N2 - Cure models are used in time-to-event analysis when not all individuals are expected to experience the event of interest, or when the survival of the considered individuals reaches the same level as the general population. These scenarios correspond to a plateau in the survival and relative survival function, respectively. The main parameters of interest in cure models are the proportion of individuals who are cured, termed the cure proportion, and the survival function of the uncured individuals. Although numerous cure models have been proposed in the statistical literature, there is no consensus on how to formulate these. We introduce a general parametric formulation of mixture cure models and a new class of cure models, termed latent cure models, together with a general estimation framework and software, which enable fitting of a wide range of different models. Through simulations, we assess the statistical properties of the models with respect to the cure proportion and the survival of the uncured individuals. Finally, we illustrate the models using survival data on colon cancer, which typically display a plateau in the relative survival. As demonstrated in the simulations, mixture cure models which are not guaranteed to be constant after a finite time point, tend to produce accurate estimates of the cure proportion and the survival of the uncured. However, these models are very unstable in certain cases due to identifiability issues, whereas LC models generally provide stable results at the price of more biased estimates. SN - 1521-4036 UR - https://www.unboundmedicine.com/medline/citation/31957910/Generalized_parametric_cure_models_for_relative_survival L2 - https://doi.org/10.1002/bimj.201900056 DB - PRIME DP - Unbound Medicine ER -
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