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.
BS: Shanghai Jiao Tong University, Shanghai, China, 1991.
MS: University of Cincinnati, Cincinnati, OH, 1995.
PhD: Environmental Health, University of Cincinnati, Cincinnati, OH, 2000.
Statistics Causal Inference; Comparative Effectiveness Research (CER); causal mediational analyses; statistics modeling of electronic medical records and clinical registry data
Biostatistics and Epidemiology
Effect of ischemic time on pediatric heart transplantation outcomes: is it the same for all allografts?. Pediatric Transplantation. 2022; 26:e14259.
Reductions In Hospitalizations Among Children Referred To A Primary Care-Based Medical-Legal Partnership. Health Affairs. 2022; 41:341-349.
Dexamethasone Versus Prednisone in Children Hospitalized With Asthma Exacerbation. Hospital Pediatrics. 2022; 12:325-335.
Subgroup causal effect identification and estimation via matching tree. Computational Statistics and Data Analysis. 2021; 159:107188.
Early detection of high disease activity in juvenile idiopathic arthritis by sequential monitoring of patients' health-related quality of life scores. Biometrical Journal: journal of mathematical methods in biosciences. 2020; 62:1343-1356.
Evaluating Clinical Effectiveness with CF Registries. Cystic Fibrosis - Heterogeneity and Personalized Treatment. : IntechOpen; IntechOpen; 2020.
Comparative Effectiveness Research Using Electronic Health Records Data: Ensure Data Quality. SAGR Research Methods Cases. Washington, DC: Sage; 2020.