Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI Data
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 d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/4/538 |
| _version_ | 1827654134885515264 |
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| author | Xifen Huang Jinfeng Xu Yunpeng Zhou |
| author_facet | Xifen Huang Jinfeng Xu Yunpeng Zhou |
| author_sort | Xifen Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:30:14Z |
| format | Article |
| id | doaj.art-255333d90aac4f88b8c3b88968dfbf8b |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T21:30:14Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-255333d90aac4f88b8c3b88968dfbf8b2023-11-23T20:56:17ZengMDPI AGMathematics2227-73902022-02-0110453810.3390/math10040538Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI DataXifen Huang0Jinfeng Xu1Yunpeng Zhou2School of Mathematics, Yunnan Normal University, Kunming 650092, ChinaSchool of Mathematics, Yunnan Normal University, Kunming 650092, ChinaDepartment of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong, ChinaMotivated 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.https://www.mdpi.com/2227-7390/10/4/538clusteringfrailty modelsparsityMM algorithmADNI |
| spellingShingle | Xifen Huang Jinfeng Xu Yunpeng Zhou Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI Data Mathematics clustering frailty model sparsity MM algorithm ADNI |
| title | Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI Data |
| title_full | Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI Data |
| title_fullStr | Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI Data |
| title_full_unstemmed | Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI Data |
| title_short | Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI Data |
| title_sort | profile and non profile mm modeling of cluster failure time and analysis of adni data |
| topic | clustering frailty model sparsity MM algorithm ADNI |
| url | https://www.mdpi.com/2227-7390/10/4/538 |
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