Niko Kaciroti, Ph.D.
- Research Scientist, Department of Biostatistics
- Research Scientist, Center for Human Growth and Development
- Research Scientist, Center for Computational Medicine and Bioinformatics
- 300 N. Ingalls Bldg, 10-floor
- CHGD, #1027NW
- Ann Arbor, Michigan 48109
Niko Kaciroti is a Research Scientist at the Center for Human Growth and Development. He received his Ph.D. in Biostatistics from the University of Michigan. Since then he has collaborated in multidisciplinary research at the University of Michigan and with researchers from other universities in the United States and internationally. Dr. Kaciroti is a faculty member at the Center for Computational Medicine and Bioinformatics and the Center for Managing Chronic Disease. His main research interest is in using Bayesian models for analyzing longitudinal data from clinical trials with missing data, as well as using Bayesian methods for nonlinear and dynamic models. Dr. Kaciroti is an elected member of the International Statistical Institute and serves as statistical editor for the American Journal of Preventive Medicine and the International Journal of Behavior Nutrition and Physical Activity.
- Ph.D., Biostatistics, University of Michigan, 2002
- M.Sc. , Biostatistics, University of Michigan, 1994
- B.Sc. , Applied Mathematics, University of Tirana, 1991
Research Interests & Projects
- 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.
- Kang S, Little R, Kaciroti N. (2015). Missing not at random models for masked clinical trials with dropouts.Clinical Trials., 12(2), 139-148.
- Foster J, Taylor J, Kaciroti N, Nan, B. (2015). Simple approximations to optimal treatment regimes in randomized clinical trial data. Biostatistics, 16(2), 368-382.
- Elliot MR, Conlon ASC, Li Y, Kaciroti N, Taylor JMG. (2015). Surrogacy marker paradox measures in meta-analysis settings. Biostatistics, 16(2), 400-412.
- Kaciroti N, Raghunathan TE. (2014). Bayesian sensitivity analysis for incomplete data: bridging pattern-mixture and selection models for exponential family. Statistics in Medicine, 33(27), 4841-4857.
- Kaciroti N, Raghunathan T, Taylor J, Julius S. (2012). A Bayesian model for discrete time-to-event data with informative censoring. Biostatistics, 13(2), 341-354.
- Kaciroti N, Schork MA, Raghunathan TE, Julius S. (2009). A Bayesian Sensitivity Model for Intention-to-Treat Analysis of Binary Outcomes with Dropouts. Statistics in Medicine, 28(4), 572-5.
- Clark NM, Janz NK, Dodge J, Lin X, Trabert BJ, Kaciroti N, Mosca L, Wheeler JRC, Keteyian S, Jersey Liang J. (2009). Heart disease management by women: Does intervention format matter? Health Education and Behavior., 36(2), 394-409.
- Kaciroti N, Raghunathan TE, Schork MA, Clark NM. A (2008). Bayesian model for longitudinal count data with non-ignorable dropout. Journal of the Royal Statistical Society C: Applied Statistics., 57, 521-534.
- Kaciroti N, Raghunathan TE, Schork MA, Clark NM, Gong M. (2006). A Bayesian Approach for Clustered Longitudinal Ordinal Outcome with Nonignorable Missing Data: Evaluation of an Asthma Education Program. Journal of American Stat. Assoc., 101, 435-446..
- Julius S, Nesbitt S, Egan B, Weber MA, Michelson EL, Kaciroti N, Black HR, Grimm RH, Messerli FH, Oparil S, Schork MA, for the Trial of Prevention Hypertension (TROPHY) Study Investigators. (2006). Feasibility of Treating Prehypertension with an Angiotensin-Receptor Blocker. New England Journal of Medicine., 354:, 1685-97.
- American Statistical Association
- Internationall Biometric Society
- International Statistical Institute
- Royal Statistical Society