On variable selection in a semiparametric AFT mixture cure model

Motahareh Parsa (), Seyed Mahmood Taghavi-Shahri () and Ingrid Van Keilegom ()
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Motahareh Parsa: KU Leuven
Seyed Mahmood Taghavi-Shahri: University of Copenhagen
Ingrid Van Keilegom: KU Leuven

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2024, vol. 30, issue 2, No 8, 472-500

Abstract: 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.

Keywords: Accelerated failure time; Adaptive LASSO; Cure fraction; Mixture cure model; Penalized likelihood; Semiparametric estimation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10985-024-09619-w

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