Statistical Analysis of Incomplete Longitudinal Data Under Different Missing Scenarios
I have a postdoctoral researcher under Professor Sharon Xie's supervision. Our primary reserach focus is developing novel statistical methods for missing data and longitudinal data motivated by neurodegenerative diseases. Besides, I have interest in network analysis and probablistic graphical models.
In this research, we compare the performance of available-case analysis (ACA) and multiple imputation (MI) dealing with missing data in longitudinal studies. More precisely, we consider missing data occurring in longitudinal responses, covariates or both under different scenarios of missingness specified by missing data generation procedure. The missing data may depend on observed responses only, fully observed covariates, partially observed covariates, or a combination of observed responses and covariates, etc. For each scenario, extensive simulations are carried out, where the performance of ACA and MI is assessed through estimation bias and relative efficiency. All the simulations are done under the assumption of missing at random (MAR). In addition, we apply the methods to a neurodegenerative disease data set.
KeywordsLongitudinal data; missing data; multiple imputation
Commenting is now closed.
To understand health and disease today, we need new thinking and novel science —the kind we create when multiple disciplines work together from the ground up. That is why this department has put forward a bold vision in population-health science: a single academic home for biostatistics, epidemiology and informatics. LEARN MORE ABOUT US