Choosing and Specifying Endpoints and Outcomes
Section 8
Additional Resources
Resource | Description |
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Guidances | |
Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products | This draft guidance is part of the FDA’s Real-World Evidence (RWE) program and applies to clinical studies that use real-world data (RWD) sources, such as information from routine clinical practice, to derive RWE. The purpose is to provide sponsors, researchers, and other interested stakeholders with 30 considerations when proposing to use EHRs or medical claims data in clinical studies to support a regulatory decision on effectiveness or safety. |
Living Textbook Resource Chapters | |
Patient-Reported Outcomes | This resource chapter defines patient-reported outcomes and describes how to use, measure, interpret, and implement PRO measures. PRO data are used to inform and guide patient-centered care as well as clinical and health policy decision-making. |
Electronic Health Records-Based Phenotyping | This chapter defines phenotypes; describes how to find, evaluate, and implement appropriate phenotypes; recommends phenotype definitions; and describes phenotype use in the NIH Collaboratory and the Patient-Centered Outcomes Research Network (PCORnet). In the context of EHRs, a computable phenotype or simply phenotype refers to a clinical condition or characteristic that can be ascertained via a computerized query to an EHR system or clinical data repository using a defined set of data elements and logical expressions. Standardized computable phenotypes can enable large-scale PCTs across multiple health systems while ensuring reliability and reproducibility. |
White Papers | |
Resources from the Collaboratory Patient-Reported Outcomes (PRO) Core |
|
Journal Articles | |
Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory
(Richesson et al. JAMIA 2013) |
The Phenotypes, Data Standards, and Data Quality Core, present early observations from researchers implementing PCTs within large healthcare systems. The authors identify gaps in knowledge and present an informatics research agenda that includes identifying methods for the definition and appropriate application of phenotypes in diverse healthcare settings, and methods for validating both the definition and execution of EHR-based phenotypes. |
Grand Rounds | |
October 17, 2017 | Users’ Guide for Integrating Patient-Reported Outcomes in Electronic Health Records |
April 12, 2019 | Development of Harmonized Outcome Measures for Use in Research and Clinical Practice |
July 16, 2019 | Digital in Trials: Improving Participation and Enabling Novel Endpoints |
September 6, 2019 | Transforming Medical Evidence Generation with Technology-Enabled Trials |
July 17, 2020 | Living Textbook Grand Rounds Series: Choosing What to Measure and Making it Happen: Your Keys to Pragmatic Trial Success (Devon Check, PhD; Rachel Richesson, PhD) |
Podcasts | |
April 16, 2019 | Development of Harmonized Outcome Measures for Use in Research and Clinical Practice (Michelle Leavy, MPH, Elise Berliner, PhD) |
August 21, 2019 | Digital in Trials: Improving Participation and Enabling Novel Endpoints (Craig Lipset) |
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