Evidence map›Paper›PMID 32005187›Full record

ArticleBMC cancer2020

A web application for the design of multi-arm clinical trials.

Michael J Grayling, James Ms Wason

Registry-linked trialAbstract read
In one paragraph

Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05376189 (The Effect of Vitamin D Supplementation on Toll-Like Receptor), which is not on this map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing 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.

NCT05376189 phase2 / phase3unknown statusnot on this mapstarted 2022, after this paper: background citation

The Effect of Vitamin D Supplementation on Toll-Like Receptor (TLR) 2, 4, and Clinical Outcomes of Spondylitis Tuberculosis

TypeinterventionalSponsorHasanuddin UniversityRan2022 to 2023Enrolled37ConditionsSpondylitis, TuberculosisArms400 IU, 5000 IU, 10000 IU, Fixed Drug Combination
3 · Its place in the literature

Who cites it

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

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  20. The benefits of covariate adjustment for adaptive multi-arm designs.Statistical methods in medical research · 2022
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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

2 authors.

Michael J GraylingPopulation Health Sciences Institute, Newcastle, NE2 4AX, UK. michael.grayling@newcastle.ac.uk.ORCID http://orcid.org/0000-0002-0680-6668
James Ms WasonPopulation Health Sciences Institute, Newcastle, NE2 4AX, UK.

Funding

Medical Research Council MC_UU_00002/6
6 · The paper itself

Abstract

backgroundMulti-arm designs provide an effective means of evaluating several treatments within the same clinical trial. Given the large number of treatments now available for testing in many disease areas, it has been argued that their utilisation should increase. However, for any given clinical trial there are numerous possible multi-arm designs that could be used, and choosing between them can be a difficult task. This task is complicated further by a lack of available easy-to-use software for designing multi-arm trials.

resultsTo aid the wider implementation of multi-arm clinical trial designs, we have developed a web application for sample size calculation when using a variety of popular multiple comparison corrections. Furthermore, the application supports sample size calculation to control several varieties of power, as well as the determination of optimised arm-wise allocation ratios. It is built using the Shiny package in the R programming language, is free to access on any device with an internet browser, and requires no programming knowledge to use. It incorporates a variety of features to make it easier to use, including help boxes and warning messages. Using design parameters motivated by a recently completed phase II oncology trial, we demonstrate that the application can effectively determine and evaluate complex multi-arm trial designs.

conclusionsThe application provides the core information required by statisticians and clinicians to review the operating characteristics of a chosen multi-arm clinical trial design. The range of designs supported by the application is broader than other currently available software solutions. Its primary limitation, particularly from a regulatory agency point of view, is its lack of validation. However, we present an approach to efficiently confirming its results via simulation.

Indexed as

Clinical Trials as TopicData Interpretation, StatisticalHumansResearch DesignSample SizeSoftwareWeb BrowserFalse discovery rateFamilywise error-rateMultiple comparisonsOptimal designPowerSample size

Identifiers

PMID32005187
PMCPMC6995188

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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.