The CRHC’s Biostatistics Core includes experienced faculty with accomplished research funding and publication records. The PhD faculty provide statistical and design expertise, including design of trials, cluster-randomized studies, surveys, and epidemiologic studies; development and calculation of sample size and power estimates; and development and execution of statistical analysis plans.
Director, Center for Research on Health Care Data Center
Dr. Abebe's collaborative research focuses on design, conduct, and analysis of multicenter, randomized, controlled trials (RCTs), namely in polycystic kidney disease (PKD). He leads the data coordinating centers (DCCs) for the TAME-PKD clinical trial and the STERIO-SCD trial, and leads the statistical and data management cores for clinical trials in gestational diabetes (GDM2 Study; PI: Davis), heart failure and depression (Hopeful Heart; PI: Rollman), and HIV (DIPY and DC-04 studies; PI: Riddler).
Dr. Althouse has been principally interested in statistical applications for randomized controlled trials (RCT) since the beginning of his career, beginning with secondary outcomes papers from the Bypass Angioplasty Revascularization Investigation 2 Diabetes (BARI 2D) trial as a graduate student in statistics and epidemiology. He has co-authored over 100 published manuscripts in medical journals; presented at national meetings of the American Diabetes Association, American Heart Association, American Statistical Association, and recently co-led a session on the contrast between Frequentist and Bayesian statistical approaches at the 2019 Society for General Internal Medicine meeting. He is currently the Statistical Editor for Circulation: Cardiovascular Interventions as well as reviewing for Circulation: Cardiovascular Quality & Outcomes and Journal of Thoracic and Cardiovascular Surgery, and serves as Vice Chair of the American Heart Association Statistics Task Force. He is also the lead statistician for four ongoing RCT’s, spanning the fields of cardiology, nephrology, palliative care, and primary care; his primary research interest is fulfilling the CCDC mission of high-quality design, conduct, and analysis of randomized trials. He also promotes the role of social media for scientific discussion, tweeting frequently @ADAlthousePhD.
Dr. Chang has a wide range of interests in theoretical and applied statistics, including time-to-event (survival) and longitudinal data analysis, missing data (competing risks and informative dropout), causal effect modeling (propensity score and marginal structural modeling), design and analysis of observational studies and clinical trials, design and analysis of studies of biomarkers in risk prediction, dynamic prediction, and machine learning techniques. As the lead statistician or consulting statistician, she has helped investigators throughout the University develop research protocols and data analysis plans for biomedical studies and has overseen the data management and analyses. Dr. Chang encourages and promotes using the most up-to-date statistical methods. She has applied these methods to a range of investigations, including research on aging, HIV/AIDS and other infectious diseases, heart diseases, liver transplantation, health services research, and acute illness.
Dr. Rothenberger's statistical interests include time series methods, state-space modeling, functional data analysis and the analysis of functional correlations. More recently, he has developed an interest in the design, analysis and data coordination of clinical trials. He has collaborated on studies in areas such as sleep medicine, chronobiology, pregnancy care, periprosthetic joint infection, and mood and anxiety disorders.
Dr. Yabes collaborates with investigators in the Schools of Health Sciences on diverse clinical research projects, in areas such as renal disease, hematology, and pediatrics. His research has focused on the analysis of large administrative databases and the design, conduct, and analysis of clinical trials. He is involved in health services research of urologic diseases using the SEER-Medicare data and serves as a biostatistician in trials on insomnia, palliative care, and critical care. His methodological interest includes survival and competing risks-regression methods, analysis of longitudinal data, missing data techniques, and joint modeling.
Dr. Yu's research has focused on applying advanced psychometric theories such as item-response theory and structural equation modeling to health-related outcomes. She has been the lead psychometrician for the NIH Roadmap Initiative, Patient-Reported Outcome Measurement Information System (PROMIS) Pittsburgh research site since 2007. Her research interests include large survey data, secondary data analysis, psychometrics, and item-bank development for various patient-reported outcomes.