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On variable selection in a semiparametric AFT mixture cure model

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Abstract

In clinical studies, one often encounters time-to-event data that are subject to right censoring and for which a fraction of the patients under study never experience the event of interest. Such data can be modeled using cure models in survival analysis. In the presence of cure fraction, the mixture cure model is popular, since it allows to model probability to be cured (called the incidence) and the survival function of the uncured individuals (called the latency). In this paper, we develop a variable selection procedure for the incidence and latency parts of a mixture cure model, consisting of a logistic model for the incidence and a semiparametric accelerated failure time model for the latency. We use a penalized likelihood approach, based on adaptive LASSO penalties for each part of the model, and we consider two algorithms for optimizing the criterion function. Extensive simulations are carried out to assess the accuracy of the proposed selection procedure. Finally, we employ the proposed method to a real dataset regarding heart failure patients with left ventricular systolic dysfunction.

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Acknowledgements

The authors acknowledge support from the European Research Council (2016-2022, Horizon 2020 / ERC grant agreement No. 694409). For the simulations, they used the infrastructure of the Flemish Supercomputer Center (VSC). Ingrid Van Keilegom also acknowledges funding from the FWO and F.R.S.-FNRS under the Excellence of Science (EOS) programme, project ASTeRISK (grant nr. 40007517).

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Authors and Affiliations

  1. ORSTAT, KU Leuven, Naamsestraat 69, box 3500, 3000, Leuven, Belgium

    Motahareh Parsa & Ingrid Van Keilegom

  2. Department of Public Health, University of Copenhagen, Copenhagen, Denmark

    Seyed Mahmood Taghavi-Shahri

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  1. Motahareh Parsa
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  2. Seyed Mahmood Taghavi-Shahri
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  3. Ingrid Van Keilegom
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Correspondence to Motahareh Parsa.

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Parsa, M., Taghavi-Shahri, S.M. & Van Keilegom, I. On variable selection in a semiparametric AFT mixture cure model. Lifetime Data Anal 30, 472–500 (2024). https://doi.org/10.1007/s10985-024-09619-w

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  • DOI: https://doi.org/10.1007/s10985-024-09619-w

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