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Expertise
CHGD Stats Core offers expertise that can be applied to a wide variety of behavioral, developmental, and medical research areas. Our statisticians have excellent training in statistical methods, expertise in a variety of statistical packages such as SAS, R, Mplus, and WinBUGS, and extensive experience analyzing longitudinal, repeated measures, survival, and time series data, including data with missing values. The Core offers exploratory data analysis; power analysis and assistance with study design; elegant visualization and plotting of data; and statistical analysis suitable for publication.

The applied research of CHGD Stats Core is strongly interdisciplinary. Some examples of projects include the effect of iron deficiency on brain, behavior and development; obesity; managing chronic disease; hypertension and cardiovascular diseases; emotion regulation as complex systems in preschoolers; and the effect of food insecurity on psychophysiological responses to social stress.

Methods
The statistical methods offered through the Core range from qualitative data analysis to advanced quantitative methods, including mixed effects/ hierarchical/ multilevel models; latent growth curve models; structural equation models; additive regression models; and nonparametric regression models. In particular, the Core has expertise with Bayesian modeling techniques for analyzing longitudinal data from randomized clinical trials with missing values as well as for modeling nonlinear and dynamic models in a multilevel setting. In addition, computationally efficient methods for analyzing Big Data using a range of machine learning techniques are used.

Statistical Analysis Core Director
Dr. Niko Kaciroti, PhD
Research Scientist
Department of Pediatrics – Medical School

Dr. Kaciroti’s research focuses on using Bayesian modeling techniques for analyzing longitudinal data from randomized clinical trials with missing data as well as for modeling nonlinear and dynamic models in a multilevel setting. His 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.

Dr. Kaciroti’s faculty page

Core Manager and Statistician
Harlan McCaffery, MS
Statistician Intermediate
Center for Human Growth and Development

Mr. McCaffery earned his MS in Biostatistics with concentration in Statistical Methods and Practice from Northwestern University in 2018.  He has expertise in statistical programming; data visualization; statistical consulting and study design; and the application of advanced statistical methods, including mixed effects modeling, nonparametric regression modeling, and survival data analysis.  His applied research is on infant growth and development; obesity and the interaction of child/parent behaviors; and social stress in food-insecure adults.

Core Statistician
Yujie Wang, MS
Statistician Intermediate
Center for Human Growth and Development

Ms. Wang earned her MS in Applied Statistics with concentration in human development and education from Teachers College, Columbia University in 2017. She has expertise in statistical programming, statistical modeling, data visualization and study design. Prior to her work at UM, she was an analyst at the National Center for Restructuring Education, Schools, and Teaching (NCREST) at Teachers College and provided consultation to the Michigan Department of Education (MDE) regarding to the quantitative research on the effectiveness of Early Middle College (EMC) programs in Michigan.

Relevant Publications

Methodologic Papers

  1. Foster J, Nan B, Shen L, Kaciroti N, Taylor JMG. Permutation testing for treatment covariate interactions and subgroup identification. Statistics in Biosciences. 2016; 8: 77-98.
  2. Kang S, Little R, Kaciroti N. (2015). Missing not at random models for masked clinical trials with dropouts. Clinical Trials., 12(2), 139-148.
  3. 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.
  4. Elliot MR, Conlon ASC, Li Y, Kaciroti N, Taylor JMG. (2015). Surrogacy marker paradox measures in meta-analysis settings. Biostatistics, 16(2), 400-412.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.

Applied Papers

  1. Griauzde D, Lumeng J, Shah P, Kaciroti N. Lower body max index z-score trajectory during early childhood following the birth of a younger sibling. Academic Pediatrics. In press.
  2. Kaciroti N*, Yian H*, Yaping J, Xing l, Guobin X, Richards B, Ming L, Lozoff B. Inadequate iron stores in early term neonates. Journal of Perinatology. In press. *Share first authorship.
  3. Silver M, Shao J, Zhu B, Xu L, Li M, Chen M, Xia Y, Kaciroti N, Lozoff B, Meeker J. Prenatal organophosphate insecticide exposure and infant sensory function. International Journal of Hygiene and Environmental Health. In press.
  4. Brook R, Kaciroti N, Bakris G, Dahlö B, Pitt B, Velazquez E, Weber M, Zappe D, Hau T, Jamerson K for the ACCOMPLISH investigators. Prior medication and cardiovascular benefits from combination angiotensin converting enzyme inhibition plus calcium channel blockade among high-risk hypertensive patients. Journal of American Heart Association. In press.
  5. Shah P, Weeks H, Richards B, Kaciroti N. Early childhood curiosity and kindergarten reading and math academic achievement. Pediatric Research. In press.
  6. Shakkottai A, Kaciroti N, Kasmikha L, Nasr S. Impact of home spirometry on medication adherence among adolescents with cystic fibrosis. Pediatric Pulmonology. 2018; 53(4), 431-436.
  7. Lumeng J, Miller A, Appugliese D, Rosenblum K, Kaciroti N. Picky eating, pressuring feeding, and weight gain in toddlers. Appetite. 2018; 123: 299-305.
  8. Shellhaas R, Kenia P, Hassan F, Barks J, Kaciroti N, Chervin R. Sleep-disordered breathing is ubiquitous among newborns with myelomeningocele. Journal of Pediatrics. 2018;194: 244-247.
  9. Bauer K, Haines J, Miller A, Rosenblum K, Appugliese D, Lumeng J, Kaciroti N. Maternal restrictive feeding and eating in the absence of hunger among toddlers: a cohort study.2017; 14:172.
  10. Miller A, Kaciroti N, Sturza J, Retzloff L, Rosenblum K, Vazquez D, Lumeng J. Associations between stress biology indicators and overweight across toddlerhood. 2017; 79; 98-106.
  11. 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.
  12. 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.