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: | Chao Cai |
| Institution: | University of South Carolina |
| Department: | Clinical Pharmacy and Outcomes Sciences |
| Country: | |
| Proposed Analysis: | I am a faculty in clinical pharmacy and outcomes sciences research. I am interested in applying more advanced statistical method (mixture cure model) to re-evaluate the effects of anticholinergic medications and AD biomarkers on risk of developing Alzheimer’s disease using ADNI dataset as a secondary data analysis. Anticholinergic medications are used for many conditions but might associated with accelerate cognitive decline. Only a proportion of patients will develop AD if taking anticholinergic therapy. Distinguishing non-susceptible (never developing AD) from those susceptible can convey important additional information. Statistical mixture cure model is a special survival method which has ability to estimate the 'cure' rate (the proportion of subjects who will be non-susceptible to AD) and more accurately describe characteristics of the non-susceptible subjects and susceptible subjects. Mixture cure model is a validated statistical model and has been applied to various applied fields such as oncology studies. However the cure model framework has never been proposed and applied in AD research. Thus, I would like to apply for access to ADNI dataset and apply mixture cure model techniques to evaluate risk factors (determinates) of AD. |
| Additional Investigators |
