@article{3a8c57d969fd42a794da7835239bf14c,
title = "The Certainty Framework for Assessing Real-World Data in Studies of Medical Product Safety and Effectiveness",
abstract = "A fundamental question in using real-world data for clinical and regulatory decision making is: How certain must we be that the algorithm used to capture an exposure, outcome, cohort-defining characteristic, or confounder is what we intend it to be? We provide a practical framework to help researchers and regulators assess and classify the fit-for-purposefulness of real-world data by study variable for a range of data sources. The three levels of certainty (optimal, sufficient, and probable) must be considered in the context of each study variable, the specific question being studied, the study design, and the decision at hand.",
author = "Cocoros, {Noelle M} and Peter Arlett and Dreyer, {Nancy A} and Chieko Ishiguro and Solomon Iyasu and Miriam Sturkenboom and Wei Zhou and Sengwee Toh",
note = "Funding Information: No funding was received for this work. This paper received endorsement from the International Society for Pharmacoepidemiology (ISPE). The authors thank all ISPE members who provided comments to an earlier version of the manuscript. The views expressed in this article are the personal views of the authors and may not be understood or quoted as being made on behalf of or reflecting the position of the agencies or organizations with which the authors are affiliated. Publisher Copyright: {\textcopyright} 2020 The Authors Clinical Pharmacology & Therapeutics {\textcopyright} 2020 American Society for Clinical Pharmacology and Therapeutics",
year = "2021",
month = may,
doi = "10.1002/cpt.2045",
language = "English",
volume = "109",
pages = "1189--1196",
journal = "Clinical Pharmacology and Therapeutics",
issn = "0009-9236",
publisher = "Nature Publishing Group",
number = "5",
}