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: | Omar Ghabayen |
| Institution: | Jubilee Institute |
| Department: | Student Research |
| Country: | |
| Proposed Analysis: | Continuous variables will be expressed as mean and standard deviation, while categorical variables will be expressed as numbers and percentages. Data will be checked for normality using Kolmogorov-Smirnov test. The primary outcome of this analysis will be diagnosis of Alzheimer’s disease. To account for the large number of potential variables in the dataset, we are proposing to use dimension reduction before running the major analysis. This dimension reduction will make the data more informative and allow using advanced statistical analysis. Dimension reduction will be conducted using Principal Component Analysis (PCA). Both the Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett’s test of sphericity will be used to check for factorability. Components will be extracted at eigenvalues higher than 1.00 and by inspection of the breaking point in the scree plot. The Kaiser-Meyer-Oklin (KMO) sample adequacy index will be set at > 0.6. To retain the orthogonality of the measurements, the items will be rotated as appropriate. To confirm the analysis, the Horn’s parallel analysis method by running a Monte-Carlo simulation with a randomly generated set of data will be used. Resulting components will be standardized and used as independent variables in the analysis. A multivariable binary logistic regression model will be fitted to predict the diagnosis of Alzheimer’s disease. All the variables will be tested for multi-collinearity to ensure independence of variables using a corelation matrix. Odds ratios and 95% confidence intervals will be computed. Statistical significance is set at p <0.05. Both IBM SPSS and JASP software will be used to complete the analysis. |
| Additional Investigators | |
| Investigator's Name: | Mahmoud M. AbuAlSamen |
| Proposed Analysis: | Continuous variables will be expressed as mean and standard deviation, while categorical variables will be expressed as numbers and percentages. Data will be checked for normality using Kolmogorov-Smirnov test. The primary outcome of this analysis will be diagnosis of Alzheimer’s disease. To account for the large number of potential variables in the dataset, we are proposing to use dimension reduction before running the major analysis. This dimension reduction will make the data more informative and allow using advanced statistical analysis. Dimension reduction will be conducted using Principal Component Analysis (PCA). Both the Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett’s test of sphericity will be used to check for factorability. Components will be extracted at eigenvalues higher than 1.00 and by inspection of the breaking point in the scree plot. The Kaiser-Meyer-Oklin (KMO) sample adequacy index will be set at > 0.6. To retain the orthogonality of the measurements, the items will be rotated as appropriate. To confirm the analysis, the Horn’s parallel analysis method by running a Monte-Carlo simulation with a randomly generated set of data will be used. Resulting components will be standardized and used as independent variables in the analysis. A multivariable binary logistic regression model will be fitted to predict the diagnosis of Alzheimer’s disease. All the variables will be tested for multi-collinearity to ensure independence of variables using a corelation matrix. Odds ratios and 95% confidence intervals will be computed. Statistical significance is set at p <0.05. Both IBM SPSS and JASP software will be used to complete the analysis. |
