Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI Data

Xifen Huang, Jinfeng Xu and Yunpeng Zhou
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Xifen Huang: School of Mathematics, Yunnan Normal University, Kunming 650092, China
Jinfeng Xu: School of Mathematics, Yunnan Normal University, Kunming 650092, China
Yunpeng Zhou: Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong, China

Mathematics, 2022, vol. 10, issue 4, 1-21

Abstract: Motivated by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, the objective of integration of important biomarkers for the early detection of Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD) as a therapeutic intervention is most likely to be beneficial in the early stages of disease progression. Developing predictors for MCI to AD comes down to genotype variables such that the dimension of predictors increases as the sample becomes large. Thus, we consider the sparsity concept of coefficients in a high-dimensional regression model with clustered failure time data such as ADNI, which enables enhancing predictive performances and facilitates the model’s interpretability. In this study, we propose two MM algorithms (profile and non-profile) for the shared frailty survival model firstly and then extend the two proposed MM algorithms to regularized estimation in sparse high-dimensional regression model. The convergence properties of our proposed estimators are also established. Furthermore simulation studies and analysis of ADNI data are illustrated by our proposed methods.

Keywords: clustering; frailty model; sparsity; MM algorithm; ADNI (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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