Evidence map›Paper›PMID 15142261›Full record

ReviewArthritis research & therapy2004

A surplus of positive trials: weighing biases and reconsidering equipoise.

David T Felson, Leonard Glantz

Abstract readReviewComment
In one paragraph

Review in Arthritis research & therapy, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Tracking system for studies should be in place.BMJ (Clinical research ed.) · 2004
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

David T FelsonBoston University School of Medicine, Boston, Massachusetts, USA. dfelson@bu.edu
Leonard Glantz

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this issue, Fries and Krishnan raise provocative new ideas to explain the surfeit of positive industry sponsored trials evaluating new drugs. They suggest that these trials were designed after so much preliminary work that they were bound to be positive (design bias) and that this violates clinical equipoise, which they characterize as an antiquated concept that should be replaced by a focus on subject autonomy in decision making and expected value for all treatments in a trial. We contend that publication bias, more than design bias, could account for the remarkably high prevalence of positive presented trials. Furthermore, even if all new drugs were efficacious, given the likelihood of type 2 errors, not all trials would be positive. We also suggest that clinical equipoise is a nuanced concept dependent on the existence of controversy about the relative value of two treatments being compared. If there were no controversy, then trials would be both unnecessary and unethical. The proposed idea of positive expected value is intriguing, but in the real world such clearly determinable values do not exist. Neither is it clear how investigators and sponsors, who are invested in the success of a proposed therapy, would (or whether they should) develop such a formula.

Indexed as

BiasRandomized Controlled Trials as TopicResearch DesignUncertaintyHumans

Identifiers

PMID15142261
PMCPMC416455

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.