Cracking the Code: When and How to Validate ICD Algorithms for RWE
The availability of real world (e.g., routinely collected) data has allowed researchers to generate massive amounts of evidence on epidemiology, natural history, disease burden, and drug efficacy. However, very few studies conducted with these data use validated code algorithms to identify the study cohort, exposure, or control variables. Even when algorithms are validated, their performance is often suboptimal. Several research groups and government agencies have offered recommendations for when and how algorithms should be validated and how the results should be reported. Key learning objectives: - The majority of studies performed with real world data lack adequate algorithm validation. - Exposures and outcomes algorithms are often more important to validate than population identification algorithms. - Positive predictive value, while the most often reported validation statistic, may not be the most useful or important one - Validation of algorithms for rare conditions requires a different approach than for common ones. - Medical record review remains the only reliable validation method in most cases and cannot be reliably performed with artificial intelligence techniques. - Validation of code algorithms using accepted methods improves study quality and increases chance of publication acceptance at higher impact journals.
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Cracking the Code: When and How to Validate ICD Algorithms for RWE
- 1. Michael Broder, MD, MSHS President Partnership for Health Analytic Research (PHAR) Anil Vachani, MD, MS Associate Professor, Medicine University of Pennsylvania SOM Karina Raimundo, MS Director HEOR AD/NRD Genentech, Inc. Cracking the Code: When and How to Validate ICD Algorithms for RWE Eric Benchimol, MD, PhD FRCPC Professor, Pediatrics & Clinical Epidemiology University of Toronto
- 2. Cracking the Codes: When and How to Validate ICD Algorithms for RWE Michael S Broder, MD, President mbroder@pharllc.com
- 4. Clinical Experience Highly Trained Staff Methodologic Rigor
- 8. When and How to Validate ICD Algorithms for RWE
- 9. | | | Assessing EHR and Claims Data To Support Regulatory Decision-Making (FDA Draft Guidance 2021) © 2022 9 “RWD are data ... routinely collected from a variety of sources” “RWE is the evidence … derived from analysis of RWD” “Complete verification of a study variable is considered the most rigorous approach,” but if not feasible “assessing the performance of [its] operational definition might suffice” https://www.fda.gov/regulatory-information/search-fda-guidance-documents/real-world-data-assessing-electronic-health-records-and-medical-claims-data-support-regulatory
- 10. | | | Growth in Published RWD and Validation Studies 10 © 2022 430 4484 3 203 1985 1990 1995 2000 2005 2010 2015 RWD Validation
- 11. | | | Expertise. Evidence. Experience. All the right elements to make your project a success.
- 12. Thank you for participating! CLICK HERE to learn more and watch the webinar