Biostatistical Expertise for Study Design and Data Analysis

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.


Kaleab Abebe, PhD

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).

Andrew Althouse, PhD

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 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 RCTs, 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.

(Joyce) Chung-Chou H. Chang, PhD

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.

Cristina Murray-Krezan, PhD

Associate Director, Clinical Trials
Co-Director, Center for Clinical Trials and Data Coordination

Dr. Murray-Krezan’s clinical research interests are wide and varied, with a focus in recent years on substance use disorders, as well as interventions to improve the health and well-being of marginalized, underserved, and underrepresented populations. She also has an interest in women’s health. She has been involved in clinical trials research since 2007 and has an affinity for patient-centered trials with complex behavioral and medical interventions. Additionally, Dr. Murray-Krezan is dedicated to the success, growth, and integration of data coordination in multi-site clinical trials and observational studies. This includes research in the areas of novel or best study designs, recruitment and retention methods, and integration of pragmatic approaches for study outcomes. Statistical research interests include developing new methods to identify and correct for informative drop-out in the survival analytic framework.

Scott Rothenberger, PhD

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.

Tianxiu Wang, PhD

Dr. Wang has provided statistical support to investigators in health behaviors and community health studies, psychology, health policy, and pediatrics. She is interested in the design and analysis of clinical trials, survival analysis with a focus on accommodating competing risks, propensity score analysis, multiple imputation technique to handle missing data, and measurement invariance testing in psychological and developmental research.

Jonathan Yabes, PhD

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.

Lan Yu, PhD

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.