TRiPOD: Toward Reusable Phenotypes in Observational Data for AD/ADRD - managing definitions and correcting bias

Project Summary
Large observational data such as electronic health records (EHRs) and medical claims have
become an enabling source for facilitating clinical and translational research including Alzheimer's
Disease and Alzheimer's Disease Related Dementia (AD/ADRD). One major challenge for
conducting observational AD/ADRD studies is about phenotyping – there is a lack of a centralized
repository for hosting and standardizing phenotype definitions in AD/ADRD research and few
methods have been developed to address bias associated with phenotyping errors in observation
data. Therefore, the overarching goal of this proposal is to fully develop a joint effort between
medical informaticians, statisticians, clinicians, and epidemiologists with a focus on building a
rigorous set of methods and tools for managing phenotype definitions and for correcting bias in
observational data analysis, through modern knowledge engineering and data-driven statistical
modeling. To achieve that goal, we propose three specific aims in this study: (1) Aim 1 - Collect,
normalize, and share definitions of common phenotypes used in AD/ADRD observational
research; (2) Aim 2 - Develop novel algorithms to correct bias associated with phenotyping errors
when users apply existing phenotype definitions to local data; and (3) Aim 3 - Validate, refine, and
disseminate proposed methods and tools by demonstration studies and community engagement.
We believe informatics methods and tools proposed here will improve current practice on
phenotypic data management and analysis, thus enhancing the reproducibility and quality of
observational studies on AD/ADRD.

Grant Number: 5R01AG073435-05
NIH Institute/Center: NIH
Principal Investigator: Yong Chen