Niko Kaciroti, Ph.D.

Niko Kaciroti, Ph.D.

CCMB Affiliated Fculty
Research Scientist, CHGD
Research Scientist of Biostatistics

Areas of Interest

My main research interest focuses on using Bayesian modeling techniques for analyzing longitudinal data from randomized clinical trials with missing data. I have developed Bayesian models for sensitivity analysis in randomized trials for different types of outcomes including ordinal, Poisson, binary, and time-to-event data. I have applied such models in multicenter randomized trials: a) for managing asthma, and b) for preventing hypertension. Another area of research that I am also interested in is using Bayesian methods for modeling nonlinear and dynamic models in a multilevel setting, for example, cortisol data. Bayesian modeling techniques provides the flexibility to incorporate different sources of uncertainty as well as the computational advantage via MCMC to fit nonlinear and dynamic models. My applied research is related to: the effect of iron deficiency on brain, behavior and development; obesity; managing chronic disease; hypertension and cardiovascular diseases; and emotion regulation as complex systems in preschoolers.