Track: Methods in Pharmacoepidemiology -> Measurement Methods - Validation methods to confirm data (including Patient Reported Outcomes development and validation); methods to deal with missing data
(PO-4867) Developing A Structured Process To Identify Fit For Purpose Data Framework
Monday, September 14, 2020
Ulka B Campbell, Robert F Reynolds, Emily Rubinstein and Nicolle M Gatto
Background: To complement standard guidelines, the 2019 SPACE framework (A Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real‐world Evidence) elucidated a process for designing valid and transparent real-world studies. SPACE prioritized the evaluation of secondary data sources against minimal size and measurement criteria.
Objectives: To develop a structured process for conducting feasibility assessments to identify regulatory grade, fit for purpose data applied in the SPACE framework.
Methods: The process was designed and developed by researchers with expertise in pharmacoepidemiology and real-world data from industry, academia, and data analytics. The process was informed by literature reviews, discussions, and consensus among the stakeholders. Regulatory-grade and fit for purpose data were defined based on the FDA’s framework for a real-world evidence program.
Results: A structured framework for feasibility assessments with step-by-step guidance was developed. Step one requires prioritization of a small number of criteria to narrow the field of data sources. Checklist items are based on database size, structure, data availability and completeness, such as: (1) Are the data sufficiently large to achieve the needed number of patients? (2) Do the patients have sufficient length of follow-up? (3) Are therapeutic area specific variables captured (e.g. cancer stage)? (4) Are treatments administered in specialized settings captured (e.g. infusion vs. oral)? (5) Are results from procedures and biospecimen analysis captured? (6) Is data linkage possible with unstructured data (e.g. verbatim medical information)? (7) Are the data capable of automated abstraction (e.g. natural language processing)?Subsequently, in step two, the investigator uses a template to tabulate the responses from the initial feasibility questions. Step three further narrows the potential data sources by adding criteria as additional information emerges. For example, the investigator may need to identify a minimum number of patients with a given indication and prior medication use based on sample size calculations and varying assumptions. Lastly, operational considerations, such as time and budget, are factored into the decision.
Conclusions: A structured process for conducting feasibility assessments contextualizes the steps of identifying secondary data sources fit for regulatory decision making.