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: | Daewoo Pak |
| Institution: | Yonsei University/Mirae Campus |
| Department: | Division of Data Science |
| Country: | |
| Proposed Analysis: | We have developed a novel statistical tool to identify genes affecting the time to Mild Cognitive Impairment (MCI), subject to a specific censoring type, which is applicable to ANDI data. The proposed statistical tools in our study have the unique benefits to analyze ADNI data. First of all, the proposed tool can handle the baseline hazards, which in Dementia is known to have a large variation at baseline risk across patients. We are suggesting the nonparamteric approach to find the robust estimation for the baseline hazards. Secondly, the proposed method aims to identify important genes associated with the time-to-MCI in Dementia study with the unbiased estimation of the gene effects on the duration of the event. Indentifying important genes for MCI is not an easy topic in statistics field due to large genomic information in the ANDI data. We have devised a new alogrithm with several well-known penalties and the proposed approach outperforms the naïve approaches when it comes to efficiency and gene identification. Therefore, the practical utility of the proposed method to the ANDI data will be expected to show unique and valuable contributions to ANDI data application by understanding the natual history of Dementia. |
| Additional Investigators |
