Clinical Trial Generalizability Assessment in the Big Data Era: A Review

• Main Authors: Zhe He (Author), Xiang Tang (Author), Xi Yang (Author), Yi Guo (Author), Thomas J. George (Author), Neil Charness (Author), Kelsa Bartley Quan Hem (Author), William Hogan (Author), Jiang Bian (Author)
• Format: Book
• Published: Wiley, 2020-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

Abstract Clinical studies, especially randomized, controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long‐standing concern when applying trial results to real‐world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic review to understand the practice of generalizability assessment. We identified 187 relevant articles and systematically organized these studies in a taxonomy with three dimensions: (i) data availability (i.e., before or after trial (a priori vs. a posteriori generalizability)); (ii) result outputs (i.e., score vs. nonscore); and (iii) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but < 30% of studies reported positive generalizability results. As a priori generalizability can be assessed using only study design information (primarily eligibility criteria), it gives investigators a golden opportunity to adjust the study design before the trial starts. Nevertheless, < 40% of the studies in our review assessed a priori generalizability. With the wide adoption of electronic health records systems, rich real‐world patient databases are increasingly available for generalizability assessment; however, informatics tools are lacking to support the adoption of generalizability assessment practice.

Item Description

1752-8062
1752-8054
10.1111/cts.12764