Evidence map›Paper›PMID 25230772›Full record

ReviewTrials2014

Correcting for multiple-testing in multi-arm trials: is it necessary and is it done?

James M S Wason, Lynne Stecher, Adrian P Mander

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Trials, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.

0numbers the graph read from it
0cells of the map it votes in
85citing 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

85 citing papers in PubMed.

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25 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

3 authors.

James M S WasonMRC Biostatistics Unit Hub for Trials Methodology Research, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK. james.wason@mrc-bsu.cam.ac.uk.
Lynne Stecher
Adrian P Mander

Funding

Medical Research Council G0800860Medical Research Council MC_UP_1302/2Medical Research Council MR/J004979/1
6 · The paper itself

Abstract

backgroundMulti-arm trials enable the evaluation of multiple treatments within a single trial. They provide a way of substantially increasing the efficiency of the clinical development process. However, since multi-arm trials test multiple hypotheses, some regulators require that a statistical correction be made to control the chance of making a type-1 error (false-positive). Several conflicting viewpoints are expressed in the literature regarding the circumstances in which a multiple-testing correction should be used. In this article we discuss these conflicting viewpoints and review the frequency with which correction methods are currently used in practice.

methodsWe identified all multi-arm clinical trials published in 2012 by four major medical journals. Summary data on several aspects of the trial design were extracted, including whether the trial was exploratory or confirmatory, whether a multiple-testing correction was applied and, if one was used, what type it was.

resultsWe found that almost half (49%) of published multi-arm trials report using a multiple-testing correction. The percentage that corrected was higher for trials in which the experimental arms included multiple doses or regimens of the same treatments (67%). The percentage that corrected was higher in exploratory than confirmatory trials, although this is explained by a greater proportion of exploratory trials testing multiple doses and regimens of the same treatment.

conclusionsA sizeable proportion of published multi-arm trials do not correct for multiple-testing. Clearer guidance about whether multiple-testing correction is needed for multi-arm trials that test separate treatments against a common control group is required.

Indexed as

Research DesignControlled Clinical Trials as TopicData Interpretation, StatisticalHumansModels, StatisticalReproducibility of Results

Identifiers

PMID25230772
PMCPMC4177585

What OpenQuestion holds

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Registered trials

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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.