1. 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, 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
(412)

2. 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 1R01DK

3. A Decision Tool for 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

4. 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 1R25HS