The Structured Process to Identify Fit‐For‐Purpose Data: A Data Feasibility Assessment Framework

To complement real‐world evidence (RWE) guidelines, the 2019 Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real‐world Evidence (SPACE) framework elucidated a process for designing valid and transparent real‐world studies. As an extension...

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Veröffentlicht in:Clinical pharmacology and therapeutics 2022-01, Vol.111 (1), p.122-134
Hauptverfasser: Gatto, Nicolle M., Campbell, Ulka B., Rubinstein, Emily, Jaksa, Ashley, Mattox, Pattra, Mo, Jingping, Reynolds, Robert F.
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container_end_page 134
container_issue 1
container_start_page 122
container_title Clinical pharmacology and therapeutics
container_volume 111
creator Gatto, Nicolle M.
Campbell, Ulka B.
Rubinstein, Emily
Jaksa, Ashley
Mattox, Pattra
Mo, Jingping
Reynolds, Robert F.
description To complement real‐world evidence (RWE) guidelines, the 2019 Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real‐world Evidence (SPACE) framework elucidated a process for designing valid and transparent real‐world studies. As an extension to SPACE, here, we provide a structured framework for conducting feasibility assessments—a step‐by‐step guide to identify decision grade, fit‐for‐purpose data, which complements the United States Food and Drug Administration (FDA)’s framework for a RWE program. The process was informed by our collective experience conducting systematic feasibility assessments of existing data sources for pharmacoepidemiology studies to support regulatory decisions. Used with the SPACE framework, the Structured Process to Identify Fit‐For‐Purpose Data (SPIFD) provides a systematic process for conducting feasibility assessments to determine if a data source is fit for decision making, helping ensure justification and transparency throughout study development, from articulation of a specific and meaningful research question to identification of fit‐for‐purpose data and study design.
doi_str_mv 10.1002/cpt.2466
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subjects COVID-19 - drug therapy
Data Collection
Decision Making
Feasibility Studies
Humans
Research Design
Review
Reviews
Varenicline - adverse effects
title The Structured Process to Identify Fit‐For‐Purpose Data: A Data Feasibility Assessment Framework
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