ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Lili Tian |
| Institution: | SUNY at Buffalo |
| Department: | Biostatistics |
| Country: | |
| Proposed Analysis: | The statistical estimation and inferences of diagnostic accuracy in the literature have been largely focused on the case when subjects are categorized in a binary fashion, i.e., healthy and diseased [1,2,3]. In practice, there exist many disease processes with three or more ordinal disease classes. For example, mild cognitive impairment (MCI) and/or early stage Alzheimer's disease is a transitional stage between the cognitive changes of normal aging and the more serious problems caused by the Alzheimer's disease (AD) [4]. The proposed research will focus on developing statistical methodologies to analyze data from Alzheimer's Disease Neuroimaging Initiative (ADNI). Specifically, we propose the following research aims: Aim 1: Develop new measures for statistical diagnostic accuracy for data with three disease categories (i.e. control, MCI, AD). Aim 2: Propose new statistical method for determining cut-off points identifying three disease categories. Our proposed research aims will be achieved by developing a variety of parametric or nonparametric statistical method. Extensive simulations studies will be performed to assess the performance of the proposed methods. The proposed methods will be applied to the data from Alzheimer's Disease Neuroimaging Initiative (ADNI). References 1. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143: 29-36. 2. Swets JA. Measuring the accuracy of diagnostic systems. Science. 1988;240:1285. 3. Metz CE. Basic principles of ROC analysis. Seminars in Nuclear Medicine. 1978; 8: 283-298. 4. Xiong C, van Belle G, Miller J and Morris J (2006) Measuring and estimating diagnostic accuracy when there are three ordinal diagnostic groups. Statistics in Medicine, 25, 1251-1273. |
| Additional Investigators | |
| Investigator's Name: | Tuochuan Dong |
| Proposed Analysis: | ROC analysis for multiple class data |
| Investigator's Name: | Dan Wang |
| Proposed Analysis: | ROC analysis with genomic data |