A review of stakeholder recommendations for defining fit-for-purpose real-world evidence algorithms

Author:

Beyrer Julie1ORCID,Abedtash Hamed1ORCID,Hornbuckle Kenneth1ORCID,Murray James F1

Affiliation:

1. Department of Value, Evidence, and Outcomes (VEO) & Global Patient Safety, Eli Lilly & Company, Indianapolis, IN 46285, USA

Abstract

Aim: The credibility and value of real-world evidence (RWE) are either supported or undermined by the algorithms (i.e., operational definitions) used. Methods: We conducted a targeted evidence review of key RWE decision makers' published recommendations on RWE algorithms through April 2021. Stakeholders were regulatory bodies, other governmental agencies and payer organizations. Results: Our review identified recommended criteria: relevance, validity, reliability, responsiveness, transparency and replicability, safety, feasibility and quality process. Stakeholders routinely recommended accuracy measures, subgroups evaluation and specific considerations for assessing exposures and covariates and the underlying real-world data (RWD) quality. Conclusion: The importance of stakeholder guidance on fit-for-purpose RWE algorithms is growing. We highlight gaps that future guidance and stakeholder recommendations could address.

Publisher

Becaris Publishing Limited

Subject

Health Policy

Reference67 articles.

1. Good practices for real-world data studies of treatment and/or comparative effectiveness: recommendations from the joint ISPOR-ISPE Special Task Force on real-world evidence in health care decision making;Berger ML;Value Health,2017

2. Reporting to improve reproducibility and facilitate validity assessment for healthcare database studies V1.0;Wang SV;Value Health,2017

3. Daniel G Silcox C Bryan J McClellan M Romine M Frank K. Characterizing RWD quality and relevancy for regulatory purposes. Duke-Margolis Center for Health Policy Washington DC USA (2018) (Accessed 8 May 2021). https://healthpolicy.duke.edu/sites/default/files/2020-03/characterizing_rwd.pdf

4. Mahendraratnam N Eckert J Mercon K Understanding the need for non-interventional studies using secondary data to generate real-world evidence for regulatory decision making and demonstrating their credibility (2019) (Accessed 8 May 2021). https://healthpolicy.duke.edu/publications/understanding-need-non-interventional-studies-using-secondary-data-generate-real-world

5. Mahendraratnam N Silcox C Mercon K Determining real-world data's fitness for use and the role of reliability (2019) (Accessed 8 May 2021). https://healthpolicy.duke.edu/publications/determining-real-world-datas-fitness-use-and-role-reliability

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