Determining a Bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes
Background/Aims: Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel...
Ausführliche Beschreibung
Autor*in: |
Anna Heath [verfasserIn] Martin Offringa [verfasserIn] Petros Pechlivanoglou [verfasserIn] Juan David Rios [verfasserIn] Terry P. Klassen [verfasserIn] Naveen Poonai [verfasserIn] Eleanor Pullenayegum [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Übergeordnetes Werk: |
In: Contemporary Clinical Trials Communications - Elsevier, 2017, 18(2020), Seite 100561- |
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Übergeordnetes Werk: |
volume:18 ; year:2020 ; pages:100561- |
Links: |
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DOI / URN: |
10.1016/j.conctc.2020.100561 |
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Katalog-ID: |
DOAJ039303438 |
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520 | |a Background/Aims: Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the “inferior” intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end of the trial is unlikely, stopping for futility should be considered. To date, limited examples are available to guide the development of stopping rules for non-inferiority trials. Methods: We used a Bayesian predictive power approach to develop a stopping rule for futility for a trial collecting binary outcomes. We evaluated the frequentist operating characteristics of the stopping rule to ensure control of the Type I and Type II error. Our case study is the Intranasal Ketamine for Procedural Sedation trial (INK trial), a non-inferiority trial designed to assess the sedative properties of ketamine administered using two alternative routes. Results: We considered implementing our stopping rule after the INK trial enrols 140 patients out of 560. The trial would be stopped if 12 more patients experience a failure on the novel treatment compared to standard care. This trial has a type I error rate of 2.2% and a power of 80%. Conclusions: Stopping for futility in non-inferiority trials reduces exposure to ineffective treatments and preserves resources for alternative research questions. Futility stopping rules based on Bayesian predictive power are easy to implement and align with trial aims. Trial registration: ClinicalTrials.gov NCT02828566 July 11, 2016. | ||
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10.1016/j.conctc.2020.100561 doi (DE-627)DOAJ039303438 (DE-599)DOAJ6a045c7818df4d5387d9c2ebf1b74968 DE-627 ger DE-627 rakwb eng R5-920 Anna Heath verfasserin aut Determining a Bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background/Aims: Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the “inferior” intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end of the trial is unlikely, stopping for futility should be considered. To date, limited examples are available to guide the development of stopping rules for non-inferiority trials. Methods: We used a Bayesian predictive power approach to develop a stopping rule for futility for a trial collecting binary outcomes. We evaluated the frequentist operating characteristics of the stopping rule to ensure control of the Type I and Type II error. Our case study is the Intranasal Ketamine for Procedural Sedation trial (INK trial), a non-inferiority trial designed to assess the sedative properties of ketamine administered using two alternative routes. Results: We considered implementing our stopping rule after the INK trial enrols 140 patients out of 560. The trial would be stopped if 12 more patients experience a failure on the novel treatment compared to standard care. This trial has a type I error rate of 2.2% and a power of 80%. Conclusions: Stopping for futility in non-inferiority trials reduces exposure to ineffective treatments and preserves resources for alternative research questions. Futility stopping rules based on Bayesian predictive power are easy to implement and align with trial aims. Trial registration: ClinicalTrials.gov NCT02828566 July 11, 2016. Non-inferiority trial Bayesian predictive power Stopping rule Procedural sedation Trial design Medicine (General) Martin Offringa verfasserin aut Petros Pechlivanoglou verfasserin aut Juan David Rios verfasserin aut Terry P. Klassen verfasserin aut Naveen Poonai verfasserin aut Eleanor Pullenayegum verfasserin aut In Contemporary Clinical Trials Communications Elsevier, 2017 18(2020), Seite 100561- (DE-627)86138069X (DE-600)2858947-6 24518654 nnns volume:18 year:2020 pages:100561- https://doi.org/10.1016/j.conctc.2020.100561 kostenfrei https://doaj.org/article/6a045c7818df4d5387d9c2ebf1b74968 kostenfrei http://www.sciencedirect.com/science/article/pii/S2451865420300454 kostenfrei https://doaj.org/toc/2451-8654 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 18 2020 100561- |
spelling |
10.1016/j.conctc.2020.100561 doi (DE-627)DOAJ039303438 (DE-599)DOAJ6a045c7818df4d5387d9c2ebf1b74968 DE-627 ger DE-627 rakwb eng R5-920 Anna Heath verfasserin aut Determining a Bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background/Aims: Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the “inferior” intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end of the trial is unlikely, stopping for futility should be considered. To date, limited examples are available to guide the development of stopping rules for non-inferiority trials. Methods: We used a Bayesian predictive power approach to develop a stopping rule for futility for a trial collecting binary outcomes. We evaluated the frequentist operating characteristics of the stopping rule to ensure control of the Type I and Type II error. Our case study is the Intranasal Ketamine for Procedural Sedation trial (INK trial), a non-inferiority trial designed to assess the sedative properties of ketamine administered using two alternative routes. Results: We considered implementing our stopping rule after the INK trial enrols 140 patients out of 560. The trial would be stopped if 12 more patients experience a failure on the novel treatment compared to standard care. This trial has a type I error rate of 2.2% and a power of 80%. Conclusions: Stopping for futility in non-inferiority trials reduces exposure to ineffective treatments and preserves resources for alternative research questions. Futility stopping rules based on Bayesian predictive power are easy to implement and align with trial aims. Trial registration: ClinicalTrials.gov NCT02828566 July 11, 2016. Non-inferiority trial Bayesian predictive power Stopping rule Procedural sedation Trial design Medicine (General) Martin Offringa verfasserin aut Petros Pechlivanoglou verfasserin aut Juan David Rios verfasserin aut Terry P. Klassen verfasserin aut Naveen Poonai verfasserin aut Eleanor Pullenayegum verfasserin aut In Contemporary Clinical Trials Communications Elsevier, 2017 18(2020), Seite 100561- (DE-627)86138069X (DE-600)2858947-6 24518654 nnns volume:18 year:2020 pages:100561- https://doi.org/10.1016/j.conctc.2020.100561 kostenfrei https://doaj.org/article/6a045c7818df4d5387d9c2ebf1b74968 kostenfrei http://www.sciencedirect.com/science/article/pii/S2451865420300454 kostenfrei https://doaj.org/toc/2451-8654 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 18 2020 100561- |
allfields_unstemmed |
10.1016/j.conctc.2020.100561 doi (DE-627)DOAJ039303438 (DE-599)DOAJ6a045c7818df4d5387d9c2ebf1b74968 DE-627 ger DE-627 rakwb eng R5-920 Anna Heath verfasserin aut Determining a Bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background/Aims: Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the “inferior” intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end of the trial is unlikely, stopping for futility should be considered. To date, limited examples are available to guide the development of stopping rules for non-inferiority trials. Methods: We used a Bayesian predictive power approach to develop a stopping rule for futility for a trial collecting binary outcomes. We evaluated the frequentist operating characteristics of the stopping rule to ensure control of the Type I and Type II error. Our case study is the Intranasal Ketamine for Procedural Sedation trial (INK trial), a non-inferiority trial designed to assess the sedative properties of ketamine administered using two alternative routes. Results: We considered implementing our stopping rule after the INK trial enrols 140 patients out of 560. The trial would be stopped if 12 more patients experience a failure on the novel treatment compared to standard care. This trial has a type I error rate of 2.2% and a power of 80%. Conclusions: Stopping for futility in non-inferiority trials reduces exposure to ineffective treatments and preserves resources for alternative research questions. Futility stopping rules based on Bayesian predictive power are easy to implement and align with trial aims. Trial registration: ClinicalTrials.gov NCT02828566 July 11, 2016. Non-inferiority trial Bayesian predictive power Stopping rule Procedural sedation Trial design Medicine (General) Martin Offringa verfasserin aut Petros Pechlivanoglou verfasserin aut Juan David Rios verfasserin aut Terry P. Klassen verfasserin aut Naveen Poonai verfasserin aut Eleanor Pullenayegum verfasserin aut In Contemporary Clinical Trials Communications Elsevier, 2017 18(2020), Seite 100561- (DE-627)86138069X (DE-600)2858947-6 24518654 nnns volume:18 year:2020 pages:100561- https://doi.org/10.1016/j.conctc.2020.100561 kostenfrei https://doaj.org/article/6a045c7818df4d5387d9c2ebf1b74968 kostenfrei http://www.sciencedirect.com/science/article/pii/S2451865420300454 kostenfrei https://doaj.org/toc/2451-8654 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 18 2020 100561- |
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determining a bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes |
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Determining a Bayesian predictive power stopping rule for futility in a non-inferiority trial with binary outcomes |
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Background/Aims: Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the “inferior” intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end of the trial is unlikely, stopping for futility should be considered. To date, limited examples are available to guide the development of stopping rules for non-inferiority trials. Methods: We used a Bayesian predictive power approach to develop a stopping rule for futility for a trial collecting binary outcomes. We evaluated the frequentist operating characteristics of the stopping rule to ensure control of the Type I and Type II error. Our case study is the Intranasal Ketamine for Procedural Sedation trial (INK trial), a non-inferiority trial designed to assess the sedative properties of ketamine administered using two alternative routes. Results: We considered implementing our stopping rule after the INK trial enrols 140 patients out of 560. The trial would be stopped if 12 more patients experience a failure on the novel treatment compared to standard care. This trial has a type I error rate of 2.2% and a power of 80%. Conclusions: Stopping for futility in non-inferiority trials reduces exposure to ineffective treatments and preserves resources for alternative research questions. Futility stopping rules based on Bayesian predictive power are easy to implement and align with trial aims. Trial registration: ClinicalTrials.gov NCT02828566 July 11, 2016. |
abstractGer |
Background/Aims: Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the “inferior” intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end of the trial is unlikely, stopping for futility should be considered. To date, limited examples are available to guide the development of stopping rules for non-inferiority trials. Methods: We used a Bayesian predictive power approach to develop a stopping rule for futility for a trial collecting binary outcomes. We evaluated the frequentist operating characteristics of the stopping rule to ensure control of the Type I and Type II error. Our case study is the Intranasal Ketamine for Procedural Sedation trial (INK trial), a non-inferiority trial designed to assess the sedative properties of ketamine administered using two alternative routes. Results: We considered implementing our stopping rule after the INK trial enrols 140 patients out of 560. The trial would be stopped if 12 more patients experience a failure on the novel treatment compared to standard care. This trial has a type I error rate of 2.2% and a power of 80%. Conclusions: Stopping for futility in non-inferiority trials reduces exposure to ineffective treatments and preserves resources for alternative research questions. Futility stopping rules based on Bayesian predictive power are easy to implement and align with trial aims. Trial registration: ClinicalTrials.gov NCT02828566 July 11, 2016. |
abstract_unstemmed |
Background/Aims: Non-inferiority trials investigate whether a novel intervention, which typically has other benefits (i.e., cheaper or safer), has similar clinical effectiveness to currently available treatments. In situations where interim evidence in a non-inferiority trial suggests that the novel treatment is truly inferior, ethical concerns with continuing randomisation to the “inferior” intervention are raised. Thus, if interim data indicate that concluding non-inferiority at the end of the trial is unlikely, stopping for futility should be considered. To date, limited examples are available to guide the development of stopping rules for non-inferiority trials. Methods: We used a Bayesian predictive power approach to develop a stopping rule for futility for a trial collecting binary outcomes. We evaluated the frequentist operating characteristics of the stopping rule to ensure control of the Type I and Type II error. Our case study is the Intranasal Ketamine for Procedural Sedation trial (INK trial), a non-inferiority trial designed to assess the sedative properties of ketamine administered using two alternative routes. Results: We considered implementing our stopping rule after the INK trial enrols 140 patients out of 560. The trial would be stopped if 12 more patients experience a failure on the novel treatment compared to standard care. This trial has a type I error rate of 2.2% and a power of 80%. Conclusions: Stopping for futility in non-inferiority trials reduces exposure to ineffective treatments and preserves resources for alternative research questions. Futility stopping rules based on Bayesian predictive power are easy to implement and align with trial aims. Trial registration: ClinicalTrials.gov NCT02828566 July 11, 2016. |
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