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

Clin Transl Sci. 2020 Jul;13(4):675-684. doi: 10.1111/cts.12764. Epub 2020 Apr 10.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Systematic Review

MeSH terms

  • Big Data*
  • Databases, Factual / standards
  • Databases, Factual / statistics & numerical data
  • Electronic Health Records / standards
  • Electronic Health Records / statistics & numerical data
  • Evidence-Based Medicine / methods*
  • Evidence-Based Medicine / standards
  • Evidence-Based Medicine / statistics & numerical data
  • Humans
  • Patient Selection
  • Practice Guidelines as Topic
  • Randomized Controlled Trials as Topic / standards*
  • Research Design / standards*