Evidence mapPaperPMID 32058639Full record

SynthesisClinical and translational science2020

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

Zhe He, Xiang Tang, Xi Yang, Yi Guo, Thomas J George, Neil Charness, Kelsa Bartley Quan Hem, William Hogan, Jiang Bian

Abstract readSystematic Review
In one paragraph

Synthesis in Clinical and translational science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 6 pooled it
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

76 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Article
  9. Review
  10. Does inhaled nitric oxide treatment of pulmonary hypertension in extremely premature infants improve patient outcomes?Journal of perinatology : official journal of the California Perinatal Association · 2026
    Article
  11. Article
  12. Review
  13. Review
  14. Determinants of publication likelihood and timeliness for clinical studies.Journal of clinical and translational science · 2026
    Article
  15. Article
  16. [Generalisability of Phase III Clinical Trials Using the Example of Two German Multiple Sclerosis Registries].Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany)) · 2025
    Article
  17. Article
  18. Article
  19. Article
  20. Review

16 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zhe HeSchool of Information, Florida State University, Tallahassee, Florida, USA.ORCID 0000-0003-3608-0244
Xiang TangDepartment of Statistics, Florida State University, Tallahassee, Florida, USA.
Xi YangDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Yi GuoDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Thomas J GeorgeHematology & Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA.ORCID 0000-0002-6249-9180
Neil CharnessDepartment of Psychology, Florida State University, Tallahassee, Florida, USA.
Kelsa Bartley Quan HemCalder Memorial Library, Miller School of Medicine, University of Miami, Miami, Florida, USA.ORCID 0000-0002-8021-8469
William HoganDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.ORCID 0000-0002-9881-1017
Jiang BianDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.

Funding

University of Florida Older Americans Independence Center (OAIC)P30AG028740 · NIA · UNIVERSITY OF FLORIDA · 2022 to 2025
$4.3M
NCATS NIH HHS UL1 TR001427NIA NIH HHS P30 AG028740NIA NIH HHS R21 AG061431
6 · The paper itself

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.

Indexed as

Big DataDatabases, FactualElectronic Health RecordsEvidence-Based MedicineHumansPatient SelectionPractice Guidelines as TopicRandomized Controlled Trials as TopicResearch Design

Identifiers

PMID32058639
PMCPMC7359942

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.