Evidence map›Paper›PMID 19910921›Full record

ArticleCancer control : journal of the Moffitt Cancer Center2009

The paradox of equipoise: the principle that drives and limits therapeutic discoveries in clinical research.

Benjamin Djulbegovic

Abstract read
In one paragraph

Article in Cancer control : journal of the Moffitt Cancer Center, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
4.9field-weighted citation impact, top 5% 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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 72 citations in OpenAlex.

  1. Pooled it
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  7. Review of Clinical Equipoise: Examples from Oncology Trials.Current reviews in clinical and experimental pharmacology · 2023
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  15. New treatments compared to established treatments in randomized trials.The Cochrane database of systematic reviews · 2012
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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

1 author at 1 institution in 1 country.

Benjamin DjulbegovicCenter for Evidence-Based Medicine and Health Outcomes Research and the Clinical Translational Science Institute, the University of South Florida, Tampa, Florida 33612, USA. bdjulbeg@health.usf.edu
Moffitt Cancer Center · US

Funding

Treatment Success and Ethical Principle of EquipoiseR01CA140408 · NCI · UNIVERSITY OF SOUTH FLORIDA · PI DJULBEGOVIC, BENJAMIN · 2009 to 2010
$631k
Quality of Research on Treatment Harms in CancerR01CA133594 · NCI · UNIVERSITY OF SOUTH FLORIDA · PI DJULBEGOVIC, BENJAMIN · 2007 to 2008
$539k
Evaluation of the Quality of Clinical TrialsR01NS052956 · NINDS · H. LEE MOFFITT CANCER CTR &RES INST · PI DJULBEGOVIC, BENJAMIN · 2005 to 2006
$445k
Equipoise and the research integrity of clinical trialsR01NS044417 · NINDS · UNIVERSITY OF SOUTH FLORIDA · PI DJULBEGOVIC, BENJAMIN · 2002 to 2003
$290k
NCI NIH HHS 1R01CA133594-01NCI NIH HHS R01 CA133594NCI NIH HHS R01 CA140408NINDS NIH HHS 1R01NS044417-01NINDS NIH HHS 1 R01 NS052956-01NINDS NIH HHS R01 NS044417NINDS NIH HHS R01 NS052956
6 · The paper itself

Abstract

backgroundProgress in clinical medicine relies on the willingness of patients to take part in experimental clinical trials, particularly randomized controlled trials (RCTs). Before agreeing to enroll in clinical trials, patients require guarantees that they will not knowingly be harmed and will have the best possible chances of receiving the most favorable treatments. This guarantee is provided by the acknowledgment of uncertainty (equipoise), which removes ethical dilemmas and makes it easier for patients to enroll in clinical trials.

methodsSince the design of clinical trials is mostly affected by clinical equipoise, the "clinical equipoise hypothesis" has been postulated. If the uncertainty requirement holds, this means that investigators cannot predict what they are going to discover in any individual trial that they undertake. In some instances, new treatments will be superior to standard treatments, while in others, standard treatments will be superior to experimental treatments, and in still others, no difference will be detected between new and standard treatments. It is hypothesized that there must be a relationship between the overall pattern of treatment successes and the uncertainties that RCTs are designed to address.

resultsAn analysis of published trials shows that the results cannot be predicted at the level of individual trials. However, the results also indicate that the overall pattern of discovery of treatment success across a series of trials is predictable and is consistent with clinical equipoise hypothesis. The analysis shows that we can discover no more than 25% to 50% of successful treatments when they are tested in RCTs. The analysis also indicates that this discovery rate is optimal in helping to preserve the clinical trial system; a high discovery rate (eg, a 90% to 100% probability of success) is neither feasible nor desirable since under these circumstances, neither the patient nor the researcher has an interest in randomization. This in turn would halt the RCT system as we know it.

conclusionsThe "principle or law of clinical discovery" described herein predicts the efficiency of the current system of RCTs at generating discoveries of new treatments. The principle is derived from the requirement for uncertainty or equipoise as a precondition for RCTs, the precept that paradoxically drives discoveries of new treatments while limiting the proportion and rate of new therapeutic discoveries.

Indexed as

HumansPatient ParticipationRandomized Controlled Trials as TopicResearch Design

Identifiers

PMID19910921
PMCPMC2782889
OpenAlexW2162590828

What OpenQuestion holds

Textmetadata
LicenceTDM
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.