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Lookup NU author(s): Professor Hongsheng DaiORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Heterogeneity among patients commonly exists in clinical studies and leads to challenges in medical research. It is widely accepted that there exist various sub-types in the population and they are distinct from each other. The approach of identifying the sub-types and thus tailoring disease prevention and treatment is known as precision medicine.The mixture model is a classical statistical model to cluster the heterogeneous population into homogeneous sub-populations. However, for the highly heterogeneous population with multiple components, its parameter estimation and clustering results may be ambiguous due to the dependence of the EM algorithm on the initial values. For sub-typing purposes, the finite mixture of regression models with concomitant variables is considered and a novel statistical method is proposed to identify the main components with large proportions in the mixture sequentially. Compared to existing typical statistical inferences, the new method not only requires no pre-specification on the number of components for model fitting, but also provides more reliable parameter estimation and clustering results. Simulation studies demonstrated the superiority of the proposed method. Real data analysis on the drug response prediction illustrated its reliability in the parameter estimation and capability to identify the important subgroup.
Author(s): You N, Dai H, Wang X, Yu Q
Publication type: Article
Publication status: Published
Journal: Computational Statistics and Data Analysis
Year: 2024
Volume: 194
Print publication date: 01/06/2024
Online publication date: 23/02/2024
Acceptance date: 16/02/2024
Date deposited: 16/02/2024
ISSN (print): 0167-9473
ISSN (electronic): 1872-7352
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.csda.2024.107942
DOI: 10.1016/j.csda.2024.107942
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