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

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...
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Autor*in:	van Ravenzwaaij, Don [verfasserIn] Monden, Rei Tendeiro, Jorge N. Ioannidis, John P. A.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Bayes factors Clinical trials Statistical inference Non-inferiority designs

Anmerkung:	© The Author(s) 2019

Übergeordnetes Werk:	Enthalten in: BMC medical research methodology - London : BioMed Central, 2001, 19(2019), 1 vom: 29. März
Übergeordnetes Werk:	volume:19 ; year:2019 ; number:1 ; day:29 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s12874-019-0699-7

Katalog-ID:	SPR027376400

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520			\|a 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.
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allfields	10.1186/s12874-019-0699-7 doi (DE-627)SPR027376400 (SPR)s12874-019-0699-7-e DE-627 ger DE-627 rakwb eng van Ravenzwaaij, Don verfasserin (orcid)0000-0002-5030-4091 aut Bayes factors for superiority, non-inferiority, and equivalence designs 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Bayes factors (dpeaa)DE-He213 Clinical trials (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Non-inferiority designs (dpeaa)DE-He213 Monden, Rei aut Tendeiro, Jorge N. aut Ioannidis, John P. A. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 19(2019), 1 vom: 29. März (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:19 year:2019 number:1 day:29 month:03 https://dx.doi.org/10.1186/s12874-019-0699-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 1 29 03
spelling	10.1186/s12874-019-0699-7 doi (DE-627)SPR027376400 (SPR)s12874-019-0699-7-e DE-627 ger DE-627 rakwb eng van Ravenzwaaij, Don verfasserin (orcid)0000-0002-5030-4091 aut Bayes factors for superiority, non-inferiority, and equivalence designs 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Bayes factors (dpeaa)DE-He213 Clinical trials (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Non-inferiority designs (dpeaa)DE-He213 Monden, Rei aut Tendeiro, Jorge N. aut Ioannidis, John P. A. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 19(2019), 1 vom: 29. März (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:19 year:2019 number:1 day:29 month:03 https://dx.doi.org/10.1186/s12874-019-0699-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 1 29 03
allfields_unstemmed	10.1186/s12874-019-0699-7 doi (DE-627)SPR027376400 (SPR)s12874-019-0699-7-e DE-627 ger DE-627 rakwb eng van Ravenzwaaij, Don verfasserin (orcid)0000-0002-5030-4091 aut Bayes factors for superiority, non-inferiority, and equivalence designs 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Bayes factors (dpeaa)DE-He213 Clinical trials (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Non-inferiority designs (dpeaa)DE-He213 Monden, Rei aut Tendeiro, Jorge N. aut Ioannidis, John P. A. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 19(2019), 1 vom: 29. März (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:19 year:2019 number:1 day:29 month:03 https://dx.doi.org/10.1186/s12874-019-0699-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 1 29 03
allfieldsGer	10.1186/s12874-019-0699-7 doi (DE-627)SPR027376400 (SPR)s12874-019-0699-7-e DE-627 ger DE-627 rakwb eng van Ravenzwaaij, Don verfasserin (orcid)0000-0002-5030-4091 aut Bayes factors for superiority, non-inferiority, and equivalence designs 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Bayes factors (dpeaa)DE-He213 Clinical trials (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Non-inferiority designs (dpeaa)DE-He213 Monden, Rei aut Tendeiro, Jorge N. aut Ioannidis, John P. A. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 19(2019), 1 vom: 29. März (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:19 year:2019 number:1 day:29 month:03 https://dx.doi.org/10.1186/s12874-019-0699-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 1 29 03
allfieldsSound	10.1186/s12874-019-0699-7 doi (DE-627)SPR027376400 (SPR)s12874-019-0699-7-e DE-627 ger DE-627 rakwb eng van Ravenzwaaij, Don verfasserin (orcid)0000-0002-5030-4091 aut Bayes factors for superiority, non-inferiority, and equivalence designs 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Bayes factors (dpeaa)DE-He213 Clinical trials (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Non-inferiority designs (dpeaa)DE-He213 Monden, Rei aut Tendeiro, Jorge N. aut Ioannidis, John P. A. aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 19(2019), 1 vom: 29. März (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:19 year:2019 number:1 day:29 month:03 https://dx.doi.org/10.1186/s12874-019-0699-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2019 1 29 03
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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. 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topic_title	Bayes factors for superiority, non-inferiority, and equivalence designs Bayes factors (dpeaa)DE-He213 Clinical trials (dpeaa)DE-He213 Statistical inference (dpeaa)DE-He213 Non-inferiority designs (dpeaa)DE-He213
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title	Bayes factors for superiority, non-inferiority, and equivalence designs
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title_sort	bayes factors for superiority, non-inferiority, and equivalence designs
title_auth	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. 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. © The Author(s) 2019
abstractGer	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. © The Author(s) 2019
abstract_unstemmed	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. © The Author(s) 2019
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title_short	Bayes factors for superiority, non-inferiority, and equivalence designs
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author2	Monden, Rei Tendeiro, Jorge N. Ioannidis, John P. A.
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