There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Tian Tian |
| Institution: | University of Missouri-Columbia |
| Department: | Department of Statistics |
| Country: | |
| Proposed Analysis: | Variable selection for non-parametric additive Cox model with interval-censored failure time data. In this project, we proposed to adopt the nonparametric additive Cox model, which allows for nonlinear covariate effects. And this paper will discuss variable selection and structure estimation for this general model. For the problem, we propose a penalized sieve maximum likelihood approach with the use of Bernstein polynomials approximation and group penalization. An efficient group coordinate descent algorithm is developed to implement the proposed method and can be easily carried out for both low- and high-dimensional scenarios. Furthermore, a simulation study is performed to assess the performance of the presented approach and suggests that it works well in practice. The proposed method can be applied to an Alzheimer's disease study for identifying important and relevant genetic factors. |
| Additional Investigators |
