Division of Pharmaceutical Evaluation & Policy
Division
Pharmaceutical Evaluation & Policy
Dr. Cheng Peng is a Research Assistant Professor in the Division of Pharmaceutical Evaluation and Policy at the University of Arkansas for Medical Sciences. She holds master’s degrees in Statistics and Public Administration and a PhD in Human Development and Family Studies. Her education is further enriched by certifications in Healthcare Analytics, Quantitative Psychology, and Advanced Research Design and Methods, equipping her with advanced analytical expertise in both quantitative and qualitative research methodologies.
Dr. Cheng Peng has extensive experience in data integration, warehousing, and analytics across research and healthcare systems, including becoming Epic-certified and mastering EHR software applications. This expertise is applied in her current role at UAMS, where she also plays a pivotal role as a programmer for the Evidence-Based Prescription Drug Program, enhancing prescription plan management with innovative data-driven approaches.
Dr. Cheng Peng collaborates with a broad spectrum of researchers, including those engaged in drug discovery research at the College of Pharmacy and maternal health research at the Institute for Digital Health & Innovation. She provides expertise in statistical analysis and management of complex data sets, enabling impactful research across these disciplines.
Education & Training
- PhD in Human Development & Family Studies, Iowa State University, IA
- MS in Statistics, Iowa State University, IA
- MPA in Public Administration, Iowa State University, IA
- Certificate in Quantitative Psychology, Iowa State University, IA
- Certificate in Advanced Research Design and Methods, Iowa State University, IA
- Certificate in Cogito, Epic University, WI
- Certificate in Clarity Data Model, Epic University, WI
- Certificate in Caboodle Data Model, Epic University, WI
Research & Scholarly Interests
- Statistical modeling; Data warehousing and data integration; Data analytics and business intelligence; Healthcare claims data analysis, Epic healthcare database modeling