Evidence map›Paper›PMID 30925900›Full record

ArticleBMC medical research methodology2019

Bayes factors for superiority, non-inferiority, and equivalence designs.

Don van Ravenzwaaij, Rei Monden, Jorge N Tendeiro, John P A Ioannidis

Abstract read
In one paragraph

Article in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

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

  1. Pooled it
  2. Trial
  3. Trial
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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

4 authors.

Don van RavenzwaaijUniversity of Groningen, Department of Psychology, Grote Kruisstraat 2/1, Heymans Building, Groningen, 9712, TS, The Netherlands. d.van.ravenzwaaij@rug.nl.ORCID 0000-0002-5030-4091
Rei MondenUniversity of Groningen, Department of Psychology, Grote Kruisstraat 2/1, Heymans Building, Groningen, 9712, TS, The Netherlands.
Jorge N TendeiroUniversity of Groningen, Department of Psychology, Grote Kruisstraat 2/1, Heymans Building, Groningen, 9712, TS, The Netherlands.
John P A IoannidisDepartments of Medicine, of Health Research and Policy, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center, Stanford, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn clinical trials, study designs may focus on assessment of superiority, equivalence, or non-inferiority, of a new medicine or treatment as compared to a control. Typically, evidence in each of these paradigms is quantified with a variant of the null hypothesis significance test. A null hypothesis is assumed (null effect, inferior by a specific amount, inferior by a specific amount and superior by a specific amount, for superiority, non-inferiority, and equivalence respectively), after which the probabilities of obtaining data more extreme than those observed under these null hypotheses are quantified by p-values. Although ubiquitous in clinical testing, the null hypothesis significance test can lead to a number of difficulties in interpretation of the results of the statistical evidence.

methodsWe advocate quantifying evidence instead by means of Bayes factors and highlight how these can be calculated for different types of research design.

resultsWe illustrate Bayes factors in practice with reanalyses of data from existing published studies.

conclusionsBayes factors for superiority, non-inferiority, and equivalence designs allow for explicit quantification of evidence in favor of the null hypothesis. They also allow for interim testing without the need to employ explicit corrections for multiple testing.

Indexed as

AlgorithmsBayes TheoremResearch DesignBiometryEvidence-Based MedicineHumansOutcome Assessment, Health CareTherapeutic EquivalencyBayes factorsClinical trialsNon-inferiority designsStatistical inference

Identifiers

PMID30925900
PMCPMC6441196

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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.