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Niko Kaciroti is a Biostatistician whose primary research interest is in the development of models and methods for analyzing longitudinal and repeated measures data with missing values. He is currently working on developing and using Bayesian models and multiple imputations techniques to analyze these types of data and to adjust for the possible bias related to the missing observations. His applied research has focused on the application of statistics to medical and social science fields, specifically in implementing statistical techniques in modeling longitudinal data using linear and nonlinear mixed models and survival analysis. Dr. Kaciroti is currently collaborating with several members of CHGD on NIH and NFS funded projects related to mental health, iron deficiency, obesity and language and cognitive development. Niko Kaciroti has received a grant from RW Johnson Foundation for development of pharmacokinetic-pharmacodynamic (PK/PD) models using Bayesian framework.
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