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On a class of non‐linear transformation cure rate models
Author(s) -
Balakrishnan Narayanaswamy,
Milienos Fotios S.
Publication year - 2020
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201900005
Subject(s) - transformation (genetics) , mathematics , inflation (cosmology) , maximum likelihood , class (philosophy) , generalization , cure rate , binary data , statistics , binary number , computer science , econometrics , artificial intelligence , medicine , mathematical analysis , biochemistry , chemistry , physics , surgery , arithmetic , theoretical physics , gene
Abstract In this paper, we propose a generalization of the mixture (binary) cure rate model, motivated by the existence of a zero‐modified (inflation or deflation) distribution, on the initial number of causes, under a competing cause scenario. This non‐linear transformation cure rate model is in the same form of models studied in the past; however, following our approach, we are able to give a realistic interpretation to a specific class of proper transformation functions, for the cure rate modeling. The estimation of the parameters is then carried out using the maximum likelihood method along with a profile approach. A simulation study examines the accuracy of the proposed estimation method and the model discrimination based on the likelihood ratio test. For illustrative purposes, analysis of two real life data‐sets, one on recidivism and another on cutaneous melanoma, is also carried out.