baymedr: an R package and web application for the calculation of Bayes factors for superiority, equivalence, and non-inferiority designs.
Bayes factor
Equivalence
Non-inferiority
Superiority
baymedr
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
24 Nov 2023
24 Nov 2023
Historique:
received:
14
09
2022
accepted:
03
11
2023
medline:
27
11
2023
pubmed:
25
11
2023
entrez:
24
11
2023
Statut:
epublish
Résumé
Clinical trials often seek to determine the superiority, equivalence, or non-inferiority of an experimental condition (e.g., a new drug) compared to a control condition (e.g., a placebo or an already existing drug). The use of frequentist statistical methods to analyze data for these types of designs is ubiquitous even though they have several limitations. Bayesian inference remedies many of these shortcomings and allows for intuitive interpretations, but are currently difficult to implement for the applied researcher. We outline the frequentist conceptualization of superiority, equivalence, and non-inferiority designs and discuss its disadvantages. Subsequently, we explain how Bayes factors can be used to compare the relative plausibility of competing hypotheses. We present baymedr, an R package and web application, that provides user-friendly tools for the computation of Bayes factors for superiority, equivalence, and non-inferiority designs. Instructions on how to use baymedr are provided and an example illustrates how existing results can be reanalyzed with baymedr. Our baymedr R package and web application enable researchers to conduct Bayesian superiority, equivalence, and non-inferiority tests. baymedr is characterized by a user-friendly implementation, making it convenient for researchers who are not statistical experts. Using baymedr, it is possible to calculate Bayes factors based on raw data and summary statistics.
Sections du résumé
BACKGROUND
BACKGROUND
Clinical trials often seek to determine the superiority, equivalence, or non-inferiority of an experimental condition (e.g., a new drug) compared to a control condition (e.g., a placebo or an already existing drug). The use of frequentist statistical methods to analyze data for these types of designs is ubiquitous even though they have several limitations. Bayesian inference remedies many of these shortcomings and allows for intuitive interpretations, but are currently difficult to implement for the applied researcher.
RESULTS
RESULTS
We outline the frequentist conceptualization of superiority, equivalence, and non-inferiority designs and discuss its disadvantages. Subsequently, we explain how Bayes factors can be used to compare the relative plausibility of competing hypotheses. We present baymedr, an R package and web application, that provides user-friendly tools for the computation of Bayes factors for superiority, equivalence, and non-inferiority designs. Instructions on how to use baymedr are provided and an example illustrates how existing results can be reanalyzed with baymedr.
CONCLUSIONS
CONCLUSIONS
Our baymedr R package and web application enable researchers to conduct Bayesian superiority, equivalence, and non-inferiority tests. baymedr is characterized by a user-friendly implementation, making it convenient for researchers who are not statistical experts. Using baymedr, it is possible to calculate Bayes factors based on raw data and summary statistics.
Identifiants
pubmed: 38001458
doi: 10.1186/s12874-023-02097-y
pii: 10.1186/s12874-023-02097-y
pmc: PMC10668366
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
279Subventions
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek
ID : 016.Vidi.188.001
Informations de copyright
© 2023. The Author(s).
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