Bayes factors for superiority, non-inferiority, and equivalence designs
Abstract Background In 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...
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| Format: | Article |
| Language: | English |
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BMC
2019-03-01
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| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12874-019-0699-7 |
| _version_ | 1818198531358326784 |
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| author | Don van Ravenzwaaij Rei Monden Jorge N. Tendeiro John P. A. Ioannidis |
| author_facet | Don van Ravenzwaaij Rei Monden Jorge N. Tendeiro John P. A. Ioannidis |
| author_sort | Don van Ravenzwaaij |
| collection | DOAJ |
| description | Abstract Background In 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. Methods We advocate quantifying evidence instead by means of Bayes factors and highlight how these can be calculated for different types of research design. Results We illustrate Bayes factors in practice with reanalyses of data from existing published studies. Conclusions Bayes 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. |
| first_indexed | 2024-12-12T02:07:21Z |
| format | Article |
| id | doaj.art-654c2dff49c342d59c8884deaa7e0d09 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-12-12T02:07:21Z |
| publishDate | 2019-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-654c2dff49c342d59c8884deaa7e0d092022-12-22T00:42:01ZengBMCBMC Medical Research Methodology1471-22882019-03-0119111210.1186/s12874-019-0699-7Bayes factors for superiority, non-inferiority, and equivalence designsDon van Ravenzwaaij0Rei Monden1Jorge N. Tendeiro2John P. A. Ioannidis3University of Groningen, Department of PsychologyUniversity of Groningen, Department of PsychologyUniversity of Groningen, Department of PsychologyDepartments of Medicine, of Health Research and Policy, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation CenterAbstract Background In 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. Methods We advocate quantifying evidence instead by means of Bayes factors and highlight how these can be calculated for different types of research design. Results We illustrate Bayes factors in practice with reanalyses of data from existing published studies. Conclusions Bayes 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.http://link.springer.com/article/10.1186/s12874-019-0699-7Bayes factorsClinical trialsStatistical inferenceNon-inferiority designs |
| spellingShingle | Don van Ravenzwaaij Rei Monden Jorge N. Tendeiro John P. A. Ioannidis Bayes factors for superiority, non-inferiority, and equivalence designs BMC Medical Research Methodology Bayes factors Clinical trials Statistical inference Non-inferiority designs |
| title | Bayes factors for superiority, non-inferiority, and equivalence designs |
| title_full | Bayes factors for superiority, non-inferiority, and equivalence designs |
| title_fullStr | Bayes factors for superiority, non-inferiority, and equivalence designs |
| title_full_unstemmed | Bayes factors for superiority, non-inferiority, and equivalence designs |
| title_short | Bayes factors for superiority, non-inferiority, and equivalence designs |
| title_sort | bayes factors for superiority non inferiority and equivalence designs |
| topic | Bayes factors Clinical trials Statistical inference Non-inferiority designs |
| url | http://link.springer.com/article/10.1186/s12874-019-0699-7 |
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