A photo of Nanhua Zhang.

Associate Professor, UC Department of Pediatrics


Biography & Affiliation


The research areas I am most interested in include clinical trials, predictive modeling, electronic health record data, meta-analysis, causal inference and missing data. My research lab's primary goal is to provide statistical assistance to researchers and investigators on study design and data analysis.

I’ve collaborated with investigators on traumatic brain injury, smoking, nutrition, cystic fibrosis, HIV, inflammatory bowel diseases, quality improvement, sleep intervention and medication, epilepsy, pain therapy, emergency department mediations, environmental health and attention-deficit/hyperactivity disorder (ADHD).

With my research team, I’m also designing novel statistical processes to manage issues arising from clinical and public health studies.

My love of math and statistics led me to my research interests. Over the years, I have become truly amazed how statistics can be used to solve real-world problems. One of the most promising discoveries my team and I made was published as Early Childhood Lead Exposure and Academic Achievement in the American Journal of Public Health. The findings in this paper were among the first to link lead exposure to academic achievement.

My 2019 article, 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, in the Journal of Neurotrauma was chosen as "Top publication" of the year in the Division of Biostatistics & Epidemiology. I am also proud of three awards won by my students/mentees.

I have more than 10 years’ experience in the biostatistics field and started working at the Cincinnati Children’s Hospital Medical Center in 2013. Other accomplishments I have include publishing more than 90 peer-reviewed articles and reviewing papers for more than 50 academic journals. My research has been published in journals such as Journal of the Royal Statistical Society, Biometrics, BMC Medical Research Methodology, Journal of Neurotrauma, Pediatrics and JAMA Pediatrics.

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

Research Divisions

Biostatistics and Epidemiology


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.


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. 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.

Parent- and Adolescent-reported Executive Functioning in the Context of Randomized Controlled Trials of Online Family Problem-Solving Therapy. Fisher, AP; Gies, LM; Narad, ME; Austin, CA; Yeates, KO; Taylor, HG; Zhang, N; Wade, SL. Journal of the International Neuropsychological Society. 2021; 1-7.