ADNI | Active Investigations

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Active Investigations

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:	Pangpang Liu
Institution:	Purdue University
Department:	Management
Country:
Proposed Analysis:	Our project is about empirical likelihood for Cox regression models with high dimensions. The estimation of the coefficients in Cox regression models is easier when the number of covariates p is small. But it becomes challenging and remains little explored when p is larger than the sample size n. we are concerned with the estimation for the coefficients in Cox regression models with high dimensions where p increases as n increases. We proposed a penalized empirical likelihood method to estimate the coefficients when p>n. We need to use the ADNI dataset to conduct data analysis to illustrate the proposed method.
Additional Investigators
Investigator's Name:	Yichuan Zhao
Proposed Analysis:	Our project is about empirical likelihood for Cox regression models with high dimensions. The estimation of the coefficients in Cox regression models is easier when the number of covariates p is small. But it becomes challenging and remains little explored when p is larger than the sample size n. This paper is concerned with the estimation for the coefficients in Cox regression models with high dimensions where p increases as n increases. We proposed a penalized empirical likelihood method to estimate the coefficients when p>n. We conducted simulation studies to evaluate the performance of the proposed method in terms of coverage probabilities and powers at different censoring rates. Simulation studies show that our proposed penalized empirical likelihood for Cox regression models with high dimensions works promisingly. Now we need to use the ADNI dataset to conduct data analysis to illustrate the proposed method.