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Which test for crossing survival curves? A user’s guideline

BACKGROUND: The exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal s...

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Autores principales: Dormuth, Ina, Liu, Tiantian, Xu, Jin, Yu, Menggang, Pauly, Markus, Ditzhaus, Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8802494/
https://www.ncbi.nlm.nih.gov/pubmed/35094686
http://dx.doi.org/10.1186/s12874-022-01520-0
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author Dormuth, Ina
Liu, Tiantian
Xu, Jin
Yu, Menggang
Pauly, Markus
Ditzhaus, Marc
author_facet Dormuth, Ina
Liu, Tiantian
Xu, Jin
Yu, Menggang
Pauly, Markus
Ditzhaus, Marc
author_sort Dormuth, Ina
collection PubMed
description BACKGROUND: The exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal survival distributions in two-armed trial. The log-rank test is still the gold-standard to infer this question. However, in case of non-proportional hazards, its power can become poor and multiple extensions have been developed to overcome this issue. We aim to facilitate the choice of a test for the detection of survival differences in the case of crossing hazards. METHODS: We restricted the review to the most recent two-armed clinical oncology trials with crossing survival curves. Each data set was reconstructed using a state-of-the-art reconstruction algorithm. To ensure reproduction quality, only publications with published number at risk at multiple time points, sufficient printing quality and a non-informative censoring pattern were included. This article depicts the p-values of the log-rank and Peto-Peto test as references and compares them with nine different tests developed for detection of survival differences in the presence of non-proportional or crossing hazards. RESULTS: We reviewed 1400 recent phase III clinical oncology trials and selected fifteen studies that met our eligibility criteria for data reconstruction. After including further three individual patient data sets, for nine out of eighteen studies significant differences in survival were found using the investigated tests. An important point that reviewers should pay attention to is that 28% of the studies with published survival curves did not report the number at risk. This makes reconstruction and plausibility checks almost impossible. CONCLUSIONS: The evaluation shows that inference methods constructed to detect differences in survival in presence of non-proportional hazards are beneficial and help to provide guidance in choosing a sensible alternative to the standard log-rank test. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01520-0.
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spelling pubmed-88024942022-02-02 Which test for crossing survival curves? A user’s guideline Dormuth, Ina Liu, Tiantian Xu, Jin Yu, Menggang Pauly, Markus Ditzhaus, Marc BMC Med Res Methodol Research BACKGROUND: The exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal survival distributions in two-armed trial. The log-rank test is still the gold-standard to infer this question. However, in case of non-proportional hazards, its power can become poor and multiple extensions have been developed to overcome this issue. We aim to facilitate the choice of a test for the detection of survival differences in the case of crossing hazards. METHODS: We restricted the review to the most recent two-armed clinical oncology trials with crossing survival curves. Each data set was reconstructed using a state-of-the-art reconstruction algorithm. To ensure reproduction quality, only publications with published number at risk at multiple time points, sufficient printing quality and a non-informative censoring pattern were included. This article depicts the p-values of the log-rank and Peto-Peto test as references and compares them with nine different tests developed for detection of survival differences in the presence of non-proportional or crossing hazards. RESULTS: We reviewed 1400 recent phase III clinical oncology trials and selected fifteen studies that met our eligibility criteria for data reconstruction. After including further three individual patient data sets, for nine out of eighteen studies significant differences in survival were found using the investigated tests. An important point that reviewers should pay attention to is that 28% of the studies with published survival curves did not report the number at risk. This makes reconstruction and plausibility checks almost impossible. CONCLUSIONS: The evaluation shows that inference methods constructed to detect differences in survival in presence of non-proportional hazards are beneficial and help to provide guidance in choosing a sensible alternative to the standard log-rank test. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01520-0. BioMed Central 2022-01-30 /pmc/articles/PMC8802494/ /pubmed/35094686 http://dx.doi.org/10.1186/s12874-022-01520-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Dormuth, Ina
Liu, Tiantian
Xu, Jin
Yu, Menggang
Pauly, Markus
Ditzhaus, Marc
Which test for crossing survival curves? A user’s guideline
title Which test for crossing survival curves? A user’s guideline
title_full Which test for crossing survival curves? A user’s guideline
title_fullStr Which test for crossing survival curves? A user’s guideline
title_full_unstemmed Which test for crossing survival curves? A user’s guideline
title_short Which test for crossing survival curves? A user’s guideline
title_sort which test for crossing survival curves? a user’s guideline
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8802494/
https://www.ncbi.nlm.nih.gov/pubmed/35094686
http://dx.doi.org/10.1186/s12874-022-01520-0
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