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Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling
BACKGROUND: Inter-observer variability in stroke aetiological classification may have an effect on trial power and estimation of treatment effect. We modelled the effect of misclassification on required sample size in a hypothetical cardioembolic (CE) stroke trial. METHODS: We performed a systematic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368715/ https://www.ncbi.nlm.nih.gov/pubmed/30736833 http://dx.doi.org/10.1186/s13063-019-3222-x |
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author | Abdul-Rahim, Azmil H. Dickie, David Alexander Selvarajah, Johann R. Lees, Kennedy R. Quinn, Terence J. |
author_facet | Abdul-Rahim, Azmil H. Dickie, David Alexander Selvarajah, Johann R. Lees, Kennedy R. Quinn, Terence J. |
author_sort | Abdul-Rahim, Azmil H. |
collection | PubMed |
description | BACKGROUND: Inter-observer variability in stroke aetiological classification may have an effect on trial power and estimation of treatment effect. We modelled the effect of misclassification on required sample size in a hypothetical cardioembolic (CE) stroke trial. METHODS: We performed a systematic review to quantify the reliability (inter-observer variability) of various stroke aetiological classification systems. We then modelled the effect of this misclassification in a hypothetical trial of anticoagulant in CE stroke contaminated by patients with non-cardioembolic (non-CE) stroke aetiology. Rates of misclassification were based on the summary reliability estimates from our systematic review. We randomly sampled data from previous acute trials in CE and non-CE participants, using the Virtual International Stroke Trials Archive. We used bootstrapping to model the effect of varying misclassification rates on sample size required to detect a between-group treatment effect across 5000 permutations. We described outcomes in terms of survival and stroke recurrence censored at 90 days. RESULTS: From 4655 titles, we found 14 articles describing three stroke classification systems. The inter-observer reliability of the classification systems varied from ‘fair’ to ‘very good’ and suggested misclassification rates of 5% and 20% for our modelling. The hypothetical trial, with 80% power and alpha 0.05, was able to show a difference in survival between anticoagulant and antiplatelet in CE with a sample size of 198 in both trial arms. Contamination of both arms with 5% misclassified participants inflated the required sample size to 237 and with 20% misclassification inflated the required sample size to 352, for equivalent trial power. For an outcome of stroke recurrence using the same data, base-case estimated sample size for 80% power and alpha 0.05 was n = 502 in each arm, increasing to 605 at 5% contamination and 973 at 20% contamination. CONCLUSIONS: Stroke aetiological classification systems suffer from inter-observer variability, and the resulting misclassification may limit trial power. TRIAL REGISTRATION: Protocol available at reviewregistry540. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13063-019-3222-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6368715 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63687152019-02-15 Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling Abdul-Rahim, Azmil H. Dickie, David Alexander Selvarajah, Johann R. Lees, Kennedy R. Quinn, Terence J. Trials Research BACKGROUND: Inter-observer variability in stroke aetiological classification may have an effect on trial power and estimation of treatment effect. We modelled the effect of misclassification on required sample size in a hypothetical cardioembolic (CE) stroke trial. METHODS: We performed a systematic review to quantify the reliability (inter-observer variability) of various stroke aetiological classification systems. We then modelled the effect of this misclassification in a hypothetical trial of anticoagulant in CE stroke contaminated by patients with non-cardioembolic (non-CE) stroke aetiology. Rates of misclassification were based on the summary reliability estimates from our systematic review. We randomly sampled data from previous acute trials in CE and non-CE participants, using the Virtual International Stroke Trials Archive. We used bootstrapping to model the effect of varying misclassification rates on sample size required to detect a between-group treatment effect across 5000 permutations. We described outcomes in terms of survival and stroke recurrence censored at 90 days. RESULTS: From 4655 titles, we found 14 articles describing three stroke classification systems. The inter-observer reliability of the classification systems varied from ‘fair’ to ‘very good’ and suggested misclassification rates of 5% and 20% for our modelling. The hypothetical trial, with 80% power and alpha 0.05, was able to show a difference in survival between anticoagulant and antiplatelet in CE with a sample size of 198 in both trial arms. Contamination of both arms with 5% misclassified participants inflated the required sample size to 237 and with 20% misclassification inflated the required sample size to 352, for equivalent trial power. For an outcome of stroke recurrence using the same data, base-case estimated sample size for 80% power and alpha 0.05 was n = 502 in each arm, increasing to 605 at 5% contamination and 973 at 20% contamination. CONCLUSIONS: Stroke aetiological classification systems suffer from inter-observer variability, and the resulting misclassification may limit trial power. TRIAL REGISTRATION: Protocol available at reviewregistry540. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13063-019-3222-x) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-08 /pmc/articles/PMC6368715/ /pubmed/30736833 http://dx.doi.org/10.1186/s13063-019-3222-x Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Abdul-Rahim, Azmil H. Dickie, David Alexander Selvarajah, Johann R. Lees, Kennedy R. Quinn, Terence J. Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling |
title | Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling |
title_full | Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling |
title_fullStr | Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling |
title_full_unstemmed | Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling |
title_short | Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling |
title_sort | stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and statistical modelling |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368715/ https://www.ncbi.nlm.nih.gov/pubmed/30736833 http://dx.doi.org/10.1186/s13063-019-3222-x |
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