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Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints

BACKGROUND: Sample size calculation is a key point in the design of a randomized controlled trial. With time-to-event outcomes, it’s often based on the logrank test. We provide a sample size calculation method for a composite endpoint (CE) based on the geometric average hazard ratio (gAHR) in case t...

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Autores principales: Cortés Martínez, Jordi, Geskus, Ronald B., Kim, KyungMann, Melis, Guadalupe Gómez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101233/
https://www.ncbi.nlm.nih.gov/pubmed/33957892
http://dx.doi.org/10.1186/s12874-021-01286-x
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author Cortés Martínez, Jordi
Geskus, Ronald B.
Kim, KyungMann
Melis, Guadalupe Gómez
author_facet Cortés Martínez, Jordi
Geskus, Ronald B.
Kim, KyungMann
Melis, Guadalupe Gómez
author_sort Cortés Martínez, Jordi
collection PubMed
description BACKGROUND: Sample size calculation is a key point in the design of a randomized controlled trial. With time-to-event outcomes, it’s often based on the logrank test. We provide a sample size calculation method for a composite endpoint (CE) based on the geometric average hazard ratio (gAHR) in case the proportional hazards assumption can be assumed to hold for the components, but not for the CE. METHODS: The required number of events, sample size and power formulae are based on the non-centrality parameter of the logrank test under the alternative hypothesis which is a function of the gAHR. We use the web platform, CompARE, for the sample size computations. A simulation study evaluates the empirical power of the logrank test for the CE based on the sample size in terms of the gAHR. We consider different values of the component hazard ratios, the probabilities of observing the events in the control group and the degrees of association between the components. We illustrate the sample size computations using two published randomized controlled trials. Their primary CEs are, respectively, progression-free survival (time to progression of disease or death) and the composite of bacteriologically confirmed treatment failure or Staphylococcus aureus related death by 12 weeks. RESULTS: For a target power of 0.80, the simulation study provided mean (± SE) empirical powers equal to 0.799 (±0.004) and 0.798 (±0.004) in the exponential and non-exponential settings, respectively. The power was attained in more than 95% of the simulated scenarios and was always above 0.78, regardless of compliance with the proportional-hazard assumption. CONCLUSIONS: The geometric average hazard ratio as an effect measure for a composite endpoint has a meaningful interpretation in the case of non-proportional hazards. Furthermore it is the natural effect measure when using the logrank test to compare the hazard rates of two groups and should be used instead of the standard hazard ratio.
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spelling pubmed-81012332021-05-06 Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints Cortés Martínez, Jordi Geskus, Ronald B. Kim, KyungMann Melis, Guadalupe Gómez BMC Med Res Methodol Research BACKGROUND: Sample size calculation is a key point in the design of a randomized controlled trial. With time-to-event outcomes, it’s often based on the logrank test. We provide a sample size calculation method for a composite endpoint (CE) based on the geometric average hazard ratio (gAHR) in case the proportional hazards assumption can be assumed to hold for the components, but not for the CE. METHODS: The required number of events, sample size and power formulae are based on the non-centrality parameter of the logrank test under the alternative hypothesis which is a function of the gAHR. We use the web platform, CompARE, for the sample size computations. A simulation study evaluates the empirical power of the logrank test for the CE based on the sample size in terms of the gAHR. We consider different values of the component hazard ratios, the probabilities of observing the events in the control group and the degrees of association between the components. We illustrate the sample size computations using two published randomized controlled trials. Their primary CEs are, respectively, progression-free survival (time to progression of disease or death) and the composite of bacteriologically confirmed treatment failure or Staphylococcus aureus related death by 12 weeks. RESULTS: For a target power of 0.80, the simulation study provided mean (± SE) empirical powers equal to 0.799 (±0.004) and 0.798 (±0.004) in the exponential and non-exponential settings, respectively. The power was attained in more than 95% of the simulated scenarios and was always above 0.78, regardless of compliance with the proportional-hazard assumption. CONCLUSIONS: The geometric average hazard ratio as an effect measure for a composite endpoint has a meaningful interpretation in the case of non-proportional hazards. Furthermore it is the natural effect measure when using the logrank test to compare the hazard rates of two groups and should be used instead of the standard hazard ratio. BioMed Central 2021-05-06 /pmc/articles/PMC8101233/ /pubmed/33957892 http://dx.doi.org/10.1186/s12874-021-01286-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Cortés Martínez, Jordi
Geskus, Ronald B.
Kim, KyungMann
Melis, Guadalupe Gómez
Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints
title Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints
title_full Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints
title_fullStr Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints
title_full_unstemmed Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints
title_short Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints
title_sort using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101233/
https://www.ncbi.nlm.nih.gov/pubmed/33957892
http://dx.doi.org/10.1186/s12874-021-01286-x
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