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The Certainty Framework for Assessing Real‐World Data in Studies of Medical Product Safety and Effectiveness
Author(s) -
Cocoros Noelle M.,
Arlett Peter,
Dreyer Nancy A.,
Ishiguro Chieko,
Iyasu Solomon,
Sturkenboom Miriam,
Zhou Wei,
Toh Sengwee
Publication year - 2021
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1002/cpt.2045
Subject(s) - certainty , context (archaeology) , outcome (game theory) , variable (mathematics) , confounding , computer science , real world data , range (aeronautics) , econometrics , data mining , product (mathematics) , risk analysis (engineering) , data science , operations research , statistics , medicine , mathematics , engineering , geography , mathematical economics , geometry , mathematical analysis , archaeology , aerospace engineering
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.