Assessing Fitness for Use of Real-World Data Sources
Section 1
Introduction
Many of the real-world data sources used in clinical research are considered "secondary" sources, because the data were collected for a purpose other than the research project for which they are being used (eg, billing or clinical care). This contrasts with primary data sources, where the data are captured specifically for clinical care, billing, or a specific prospective study. (See the Acquiring Real-World Data chapter of the Living Textbook for more information about the different types of real-world data.) Consequently, before a real-world data source can be used in an analysis, one must understand its characteristics and limitations to determine whether it can be appropriately used to answer the question at hand. A given dataset may be well suited to answer a research question for a specific patient population over a certain time period, but not suitable for a different population or time frame. For this reason, data must be assessed to determine whether they are fit for their intended use or purpose prior to their use in research settings. This chapter describes several approaches that can be used to facilitate such assessments.
SECTIONS
Resources
Grand Rounds
Advances at the Intersection of Digital Health, Electronic Health Records, and Pragmatic Clinical Trials: Keys to Success in the Evolving EHR Environment; NIH Collaboratory Grand Rounds; June 26, 2020
Real-World Evidence for Drug Effectiveness Evaluation: Addressing the Credibility Gap; NIH Collaboratory Grand Rounds; October 25, 2019
Research at Scale – Exploring What is Possible with High-Quality Real-World Data. Examples from Flatiron Health; NIH Collaboratory Grand Rounds; June 15, 2018
Podcasts
Advances at the Intersection of Digital Health, Electronic Health Records, and Pragmatic Clinical Trials: Real World Evidence: Contemporary Experience and Future Directions; NIH Collaboratory Grand Rounds Podcast; May 8, 2020
Research at Scale – Exploring What is Possible with High-Quality Real-World Data. Examples from Flatiron Health; NIH Collaboratory Grand Rounds Podcast; June 22, 2018