A photo of Nanhua Zhang.

Associate Professor, UC Department of Pediatrics


Biography & Affiliation


Dr. Zhang earned his PhD in biostatistics from the University of Michigan in Ann Arbor. Prior to joining Cincinnati Children’s Hospital Medical Center, he was a faculty member at the University of South Florida in Tampa. His statistical methodology research has covered missing data, causal inference, clinical trial design, joint modeling and meta-analysis. His applied research interests include learning health systems, pediatric traumatic brain injury (TBI), environmental health, health disparity, behavioral intervention and health psychology. His research “early childhood lead exposure on academic achievement” received media coverage on TV, radio, newspapers, magazines, and various online media such as Time.com and Yahoo.com.

Dr. Zhang has authored 85 peer-reviewed papers and made numerous presentations at national and international conferences. He has served as a reviewer for over 40 different journals including: Biometrika, Statistics in Medicine, Statistics Sinica, Annals of Applied Statistics, American Journal of Public Health, Annals of Epidemiology, Health Education Research. He has taught graduate and advanced doctoral courses in linear models, survival analysis and statistical computing.

Research Interests

Missing data; comparative effectiveness; clinical trial design; meta-analysis; scale development; joint modeling; environmental health; community-based intervention; health disparity; behavioral intervention; health psychology

Academic Affiliation

Associate Professor, UC Department of Pediatrics




BS: Shanghai University of Finance and Economics, Shanghai, China.

MS: Bowling Green State University, Bowling Green, OH.

PhD: University of Michigan, Ann Arbor, MI.


Selected Publication

Examination of Injury, Host, and Social-Environmental Moderators of Online Family Problem Solving Treatment Efficacy for Pediatric Traumatic Brain Injury Using an Individual Participant Data Meta-Analytic Approach. Zhang, N; Kaizar, EE; Narad, ME; Kurowski, BG; Yeates, KO; Taylor, HG; Wade, SL. Journal of Neurotrauma. 2019; 36:1147-1155.

BAYESIAN INFERENCE FOR NONRESPONSE TWO-PHASE SAMPLING. Zhang, Y; Chen, H; Zhang, N. Statistica Sinica. 2018; 28:2167-2187.

Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis. Zhang, N; Cheng, S; Ambroggio, L; Florin, TA; Macaluso, M. BMC Medical Research Methodology. 2017; 17.

Nonrespondent Subsample Multiple Imputation in Two-Phase Sampling for Nonresponse. Zhang, N; Chen, H; Elliott, MR. Journal of Official Statistics. 2016; 32:769-785.

Subsample ignorable likelihood for accelerated failure time models with missing predictors. Zhang, N; Little, RJ. Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data. 2015; 21:457-469.

A joint model of binary and longitudinal data with non-ignorable missingness, with application to marital stress and late-life major depression in women. Zhang, N; Chen, H; Zou, Y. Journal of Applied Statistics. 2014; 41:1028-1039.

Early Childhood Lead Exposure and Academic Achievement: Evidence From Detroit Public Schools, 2008-2010. Zhang, N; Baker, HW; Tufts, M; Raymond, RE; Salihu, H; Elliott, MR. American Journal of Public Health. 2013; 103:e72-e77.

A Pseudo-Bayesian Shrinkage Approach to Regression with Missing Covariates. Zhang, N; Little, RJ. Biometrics. 2012; 68:933-942.

Subsample ignorable likelihood for regression analysis with missing data. Little, RJ; Zhang, N. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2011; 60:591-605.