Evidence map›Paper›PMID 36893990›Full record

SynthesisJournal of clinical epidemiology2023

Methodology and design of platform trials: a meta-epidemiological study.

Tyler Pitre, Samantha Cheng, Ellen Cusano, Nadia Khan, David Mikhail, Gareth Leung, Robin W M Vernooij, Christopher J Yarnell, Ewan Goligher, Srinivas Murthy and 4 more

Open access · bronzeAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of clinical epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
7.8field-weighted citation impact, top 2% of its field
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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. INCEPT: The Intensive Care Platform Trial-Design and protocol.Acta anaesthesiologica Scandinavica · 2025
    Article
  9. Article
  10. Article
  11. Review
  12. The Rise of Adaptive Platform Trials in Critical Care.American journal of respiratory and critical care medicine · 2024
    Article
  13. Article
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

14 authors at 11 institutions in 3 countries.

Tyler PitreDivision of Internal Medicine, McMaster University, Hamilton, Ontario, Canada. Electronic address: tyler.pitre@medportal.ca.
Samantha ChengFaculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
Ellen CusanoDivision of Hematology and Hematologic Malignancies, Department of Medicine, University of Calgary, Calgary, Alberta, Canada.
Nadia KhanSchulich School of Medicine and Dentistry, London, Ontario, Canada.
David MikhailFaculty of Health Sciences, McMaster University, Canada.
Gareth LeungFaculty of Medicine, University of Ottawa, Canada.
Robin W M VernooijDepartment of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Christopher J YarnellInterdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada; University Health Network and Sinai Health System, Toronto, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada.
Ewan GoligherInterdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada; Toronto General Hospital Research Institute, Toronto, Canada; Department of Physiology, University of Toronto, Toronto, Canada.
Srinivas MurthyFaculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Anna HeathChild Health Evaluative Science, The Hospital for Sick Children, Toronto, Ontario, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; Department of Statistical Science, University College London, London, UK.
Jasmine MahDepartment of medicine, Dalhousie University, Halifax, Nova Scotia, Canada.
Bram RochwergDepartment of Critical Care, Juravinski Hospital, Hamilton, Ontario, Canada; Department of Health Research Methods Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
Dena ZeraatkarDepartment of Anesthesiology, McMaster University, Hamilton, Ontario, Canada; Department of Health Research Methods Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
McMaster University · CAUniversity of Toronto · CADalhousie University · CAImpact · CAJuravinski Hospital · CAUniversity Health Network · CAUniversity of British Columbia · CAUniversity of Calgary · CAUniversity of Ottawa · CAUtrecht University · NLWestern University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAdaptive platforms allow for the evaluation of multiple interventions at a lower cost and have been growing in popularity, especially during the COVID-19 pandemic. The objective of this review is to summarize published platform trials, examine specific methodological design features among these studies, and hopefully aid readers in the evaluation and interpretation of platform trial results.

methodsWe performed a systematic review of EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), and clinicaltrials.gov from January 2015 to January 2022 for protocols or results of platform trials. Pairs of reviewers, working independently and in duplicate, collected data on trial characteristics of trial registrations, protocols, and publications of platform trials. We reported our results using total numbers and percentages, as well as medians with interquartile range (IQR) when appropriate.

resultsWe identified 15,277 unique search records and screened 14,403 titles and abstracts after duplicates were removed. We identified 98 unique randomized platform trials. Sixteen platform trials were sourced from a systematic review completed in 2019, which included platform trials reported prior to 2015. Most platform trials (n = 67, 68.3%) were registered between 2020 and 2022, coinciding with the COVID-19 pandemic. The included platform trials primarily recruited or plan to recruit patients from North America or Europe, with most subjects being recruited from the United States (n = 39, 39.7%) and the United Kingdom (n = 31, 31.6%). Bayesian methods were used in 28.6% (n = 28) of platform RCTs and frequentist methods in 66.3% (n = 65) of trials, including 1 (1%) that used methods from both paradigms. Out of the twenty-five trials with peer-reviewed publication of results, seven trials used Bayesian methods (28%), and of those, two (8%) used a predefined sample size calculation while the remainder used pre-specified probabilities of futility, harm, or benefit calculated at (pre-specified) intervals to inform decisions about stopping interventions or the entire trial. Seventeen (68%) peer-reviewed publications used frequentist methods. Out of the seven published Bayesian trials, seven (100%) reported thresholds for benefit. The threshold for benefit ranged from 80% to >99%.

conclusionWe identified and summarized key components of platform trials, including the basics of the methodological and statistical considerations. Ultimately, improving standardization and reporting in platform trials require an understanding of the current landscape. We provide the most updated and rigorous review of platform trials to date.

Indexed as

COVID-19PandemicsAdaptive Clinical Trials as TopicBayes TheoremEuropeHumansUnited KingdomAdaptive randomizationCOVID-19MethodsPlatform trialsSystematic reviewTrials

Identifiers

PMID36893990
PMCPMC9991927
OpenAlexW4323568000

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.