A photo of Bin Huang.

Bin Huang, PhD

  • Biostatistician, Division of Biostatistics and Epidemiology
  • Professor, UC Department of Pediatrics
  • UC Department of Environmental Health; UC Department of Mathematical Sciences



My research interests are statistical causal inference involving understanding the treatment effect using non-randomized study data and comparative effectiveness research. My lab research goal is to find the best overall therapy approach for patients with chronic conditions and who often spend considerable time in medical practices.

I was inspired to follow this research by the clinical and medical research colleagues at the Cincinnati Children’s Hospital Medical Center. I’m driven and passionate about finding out what works and doesn’t work for patients, and which therapies have better outcomes for certain types of patients and when to apply a particular treatment.

My colleagues and I have attained several notable findings in our lab. For example, we have designed a novel data analytical process specifically for looking at comparative effectiveness research questions by examining real-world data. Our method surpasses other multiple existing tactics in a real-world data setting. This approach is somewhat groundbreaking and provides strong, precise, effective and multipurpose factors that are especially useful for real-world data, including electronic medical records.

I believe in high-quality data and data analytical approaches being essential for high-quality research studies. These studies would boost the outcomes of the patients we serve.

My lab team and I were granted funding from the Patient-Centered Outcomes Research Institute (PCORI) to design and review our data analytical method. Our method was chosen as one of the top-ranked approaches in an international data competition. The patent is pending.

I have more than 20 years of experience in biostatistics and began working at the Cincinnati Children’s Hospital Medical Center in 1999. My research has been published in numerous journals, including JAMA Pediatrics, Thorax, Annals of translational medicine and Statistics in Medicine.



Comparative effectiveness and persistence of TNFi and non-TNFi in juvenile idiopathic arthritis: a large pediatric rheumatology center in US. Yue, X; Huang, B; Hincapie, AL; Wigle, PR; Li, Y; Qiu, T; Lovell, DJ; Morgan, EM; Guo, JJ. Rheumatology. 2021.


Subgroup causal effect identification and estimation via matching tree. Zhang, Y; Schnell, P; Song, C; Huang, B; Lu, B. Computational Statistics and Data Analysis. 2021; 159.


Evaluating Clinical Effectiveness with CF Registries. Szczesniak, R; Huang, B. Cystic Fibrosis - Heterogeneity and Personalized Treatment. 2020.


Comparative Effectiveness Research Using Electronic Health Records Data: Ensure Data Quality. Huang, B; Qiu, T; Chen, C; Neace, A; Zhang, Y; Yue, X; Zahner, J; Adams, M; Seid, M; Lovell, D; et al. . : SAGE Publications Ltd; SAGE Publications Ltd; 2020.


New Method, Same Answer: We Do Not Know if Hypertonic Saline Helps Bronchiolitis. Auger, KA; Parker, MW; Huang, B. Pediatrics. 2018; 142.


Opening the black box of neural networks: methods for interpreting neural network models in clinical applications. Zhang, Z; Beck, MW; Winkler, DA; Huang, B; Sibanda, W; Goyal, H; Tria, WA M E B-D C. Annals of Translational Medicine. 2018; 6.


Subgroup finding via Bayesian additive regression trees. Sivaganesan, S; Mueller, P; Huang, B. Statistics in Medicine. 2017; 36:2391-2403.