Evidence map›Paper›PMID 27650716›Full record

ArticleClinical pharmacology and therapeutics2016

"Threshold-crossing": A Useful Way to Establish the Counterfactual in Clinical Trials?

H-G Eichler, B Bloechl-Daum, P Bauer, F Bretz, J Brown, L V Hampson, P Honig, M Krams, H Leufkens, R Lim and 7 more

Abstract read
In one paragraph

Article in Clinical pharmacology and therapeutics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 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

30 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
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  14. Designing Clinical Trials in Wilson's Disease.Hepatology (Baltimore, Md.) · 2021
    Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Review
  20. Review
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

17 authors.

H-G EichlerEuropean Medicines Agency, London, United Kingdom.
B Bloechl-DaumDepartment of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria.
P BauerSection for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
F BretzNovartis, Basel, Switzerland.
J BrownHarvard Medical School/Harvard Pilgrim Health Care Institute, Hartford, Connecticut, USA.
L V HampsonLancaster University, Lancaster, United Kingdom.
P HonigCollegeville, Pennsylvania, USA.
M KramsJanssen Pharmaceutical Companies, Raritan, New Jersey, USA.
H LeufkensMedicines Evaluation Board, Utrecht, University of Utrecht, Utrecht, The Netherlands.
R LimHealth Canada, Ottawa, Ontario, Canada.
M M LumpkinBill and Melinda Gates Foundation, Seattle, Washington, USA.
M J MurphyProject Data Sphere, Durham, North Carolina, USA.
F PignattiEuropean Medicines Agency, London, United Kingdom.
M PoschSection for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
S SchneeweissBrigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
M TrusheimMIT Sloan School of Management, Cambridge, Massachusetts, USA.
F KoenigSection for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

Funding

Medical Research Council MR/J014079/1
6 · The paper itself

Abstract

A central question in the assessment of benefit/harm of new treatments is: how does the average outcome on the new treatment (the factual) compare to the average outcome had patients received no treatment or a different treatment known to be effective (the counterfactual)? Randomized controlled trials (RCTs) are the standard for comparing the factual with the counterfactual. Recent developments necessitate and enable a new way of determining the counterfactual for some new medicines. For select situations, we propose a new framework for evidence generation, which we call "threshold-crossing." This framework leverages the wealth of information that is becoming available from completed RCTs and from real world data sources. Relying on formalized procedures, information gleaned from these data is used to estimate the counterfactual, enabling efficacy assessment of new drugs. We propose future (research) activities to enable "threshold-crossing" for carefully selected products and indications in which RCTs are not feasible.

Indexed as

Research DesignHumansModels, TheoreticalPharmaceutical PreparationsRandomized Controlled Trials as TopicTreatment OutcomePharmaceutical Preparations

Identifiers

PMID27650716
PMCPMC5114686

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.