Research at the Intersection of Biomarkers and Causal Inference Biomarker Studies Design of Longitudinal Studies Prediction Models Surrogate Markers Causal Inference Doug Landsittel, PhD Professor of Biomedical Informatics, Biostatistics, and Clinical and Translational Science Director of Biostatistics (Research), Starzl Transplant Institute Director of the Expanding National Capacity in PCOR through Training (ENACT) Program Director of the Data Coordinating Center, Consortium for Radiological Imaging Studies of PKD (CRISP) Study Chair, Senate Educational Policies Committee and Member, Faculty Assembly Appointed Member, CDC Safety and Occupational Health Study Section Core Faculty, Comparative Effectiveness Research Center, Institute for Clinical Research Education, Center for Research on Health Care, Biomedical Informatics Core Training Faculty DougLandsittel@pitt.edu; (412) 624-0270

The Biomedical Statistics & Data Science Lab Team Members: Lead Statistician on Clinical Studies: Yaming Li, MD, MS Lead Statistician on Modeling Projects: Tiange Shi, MS Instructional Developer and Manager of Statistical Projects: Kristin Kropf, MS Teaching Fellow for Online Programs: Steven (Xiaotian) Gao, MS Administrative Assistant: Noreen Doloughty Applied Research (Collaborating Divisions/Departments): Transplant immunology and mechanistic studies (Surgery) Predictive biomarker trials (Surgery, Society for Critical Care Medicine) Clinical outcomes and physical activity in liver transplantation (Hepatology) Diagnostic and prognostic biomarker studies (Rheumatology, Nephrology, CRISP Consortium) Behavioral and medical intervention trials (Pediatrics and Rheumatology) Methodology and Education Research (Programs): Online training in patient-centered outcomes research methods (AHRQ-funded ENACT Program) Decision tools for observational comparative effectiveness research (PCORI-funded methods contract) Causal inference using propensity score-based methods

The Consortium for Radiological Imaging Studies of PKD (CRISP) ADPKD patients with steeper increases in TKV can be identified years before progressive decline in GFR. Establishing a causal link between TKV and renal function is critical for future applications of TKV as a surrogate marker. CRISP showed total kidney volume (TKV) increases exponentially in autosomal dominant polycystic kidney disease (ADPKD). CRISP also showed that TKV is prognostic of faster decline in renal function. Funding: NIDDK 1R01DK113111-01

A Decision Tool for Causal Inference and Observational Data Analysis Methods (DECODE CER) Causal inference can be expressed in terms of potential outcomes. DECODE CER is a tool in Google Slides with links to key resources. Time 0 Treatment Outcome at time 1 A Yi(A) B Yi(B) Yi(A) and Yi(B) are potential outcomes, where one is observed, and one is counterfactual. Average Causal Effect (ATE) = E[Yi(A) - Yi(B)] Funding: PCORI R-IMC-1306-03827

The Expanding National Capacity in PCOR through Training (ENACT) Program Collaboration with 6 Minority-Serving Institutions to build expertise in patient-centered outcomes research. Funding: AHRQ 1R25HS023185-01