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: | Hongmei Yu |
| Institution: | Shanxi Medical University |
| Department: | Health Statistics |
| Country: | |
| Proposed Analysis: | As a major public health issue, dementia will not only endanger health of the elderly, but put burdens heavily on patients, caregivers, societies and health care systems as well. Personalized cognitive degradation risk dynamic prediction models may enable early detection of the subjects that are subject to risk of cognitive decline so as to lay the theoretical foundation for precision medicine and optimally allocating the limited health-care resources. Based on the cognition epidemiological cohort study among the elderly people, we aimed to develop multi-state models to determine the different effects of known risk factors in the cognitive impairment process and to make prediction. The following issues should be considered simultaneously. Cognition which is the central longitudinal process in dementia natural history is not directly observed but measured by multiple psychometric tests collected repeatedly at cohort visits, which is longitudinal process. In addition, cognitive decline is very associated with onset of dementia or death, which is survival process. A further issue that should be accounted for is the strong unobservable heterogeneity between subjects that may not be explained by the covariates. A population of elderly subjects usually mixes groups of subjects with different types of cognitive trajectory (i.e. latent classes). This study aims to build a joint latent class mixed model for jointly analyzing longitudinal process and survival process to further explain heterogeneity between subjects by latent classes, to calculate individual dementia risk dynamic prediction probabilities based on cognitive measures, and to evaluate the predictive abilities for different model built strategies. This project will provide statistical support for health-care providers to identify subjects who may have unrecognized cognitive impairment or undiagnosed dementia among the elders. It will also provide model built references for multiple longitudinal markers with latent trait character and multiple terminal events. |
| Additional Investigators |
