We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI Data.
- Authors
Huang, Xifen; Xu, Jinfeng; Zhou, Yunpeng
- 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.
- Subjects
FAILURE time data analysis; ALZHEIMER'S disease; MILD cognitive impairment; REGRESSION analysis; SURVIVAL analysis (Biometry); PROGRESSION-free survival
- Publication
Mathematics (2227-7390), 2022, Vol 10, Issue 4, pN.PAG
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math10040538