Cuiling Wang has extensive experience in statistical methodological research and collaborating in clinical research, including work on neurodegenerative disease. Her expertise includes statistical methods for missing data analysis, longitudinal data analysis, survival analysis, ROC analysis, mediation analysis and power analysis. Non-random missing data due to informative drop out is a major issue in longitudinal aging studies. In collaboration with her colleague in the Einstein Aging Study (EAS), she worked on methods utilizing the auxiliary information to test the missing at random assumption and to reduce or eliminate bias due to non-random missing data. She has more than 15 years of experience working with investigators on Parkinson’s disease and on aging (including 10 years as the leader of the Statistics Core of EAS), contributing to methodological development, planning, coordinating and overseeing routine data analytic activities and conducting high-level statistical analysis and modeling.