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KMSubtraction: reconstruction of unreported subgroup survival data utilizing published Kaplan-Meier survival curves
BACKGROUND: Data from certain subgroups of clinical interest may not be presented in primary manuscripts or conference abstract presentations. In an effort to enable secondary data analyses, we propose a workflow to retrieve unreported subgroup survival data from published Kaplan-Meier (KM) plots. M...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978435/ https://www.ncbi.nlm.nih.gov/pubmed/35369867 http://dx.doi.org/10.1186/s12874-022-01567-z |
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author | Zhao, Joseph J. Syn, Nicholas L. Tan, Benjamin Kye Jyn Yap, Dominic Wei Ting Teo, Chong Boon Chan, Yiong Huak Sundar, Raghav |
author_facet | Zhao, Joseph J. Syn, Nicholas L. Tan, Benjamin Kye Jyn Yap, Dominic Wei Ting Teo, Chong Boon Chan, Yiong Huak Sundar, Raghav |
author_sort | Zhao, Joseph J. |
collection | PubMed |
description | BACKGROUND: Data from certain subgroups of clinical interest may not be presented in primary manuscripts or conference abstract presentations. In an effort to enable secondary data analyses, we propose a workflow to retrieve unreported subgroup survival data from published Kaplan-Meier (KM) plots. METHODS: We developed KMSubtraction, an R-package that retrieves patients from unreported subgroups by matching participants on KM plots of the overall cohort to participants on KM plots of a known subgroup with follow-up time. By excluding matched patients, the opposing unreported subgroup may be retrieved. Reproducibility and limits of error of the KMSubtraction workflow were assessed by comparing unmatched patients against the original survival data of subgroups from published datasets and simulations. Monte Carlo simulations were utilized to evaluate the limits of error of KMSubtraction. RESULTS: The validation exercise found no material systematic error and demonstrates the robustness of KMSubtraction in deriving unreported subgroup survival data. Limits of error were small and negligible on marginal Cox proportional hazard models comparing reconstructed and original survival data of unreported subgroups. Extensive Monte Carlo simulations demonstrate that datasets with high reported subgroup proportion (r = 0.467, p < 0.001), small dataset size (r = − 0.374, p < 0.001) and high proportion of missing data in the unreported subgroup (r = 0.553, p < 0.001) were associated with uncertainty are likely to yield high limits of error with KMSubtraction. CONCLUSION: KMSubtraction demonstrates robustness in deriving survival data from unreported subgroups. The limits of error of KMSubtraction derived from converged Monte Carlo simulations may guide the interpretation of reconstructed survival data of unreported subgroups. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01567-z. |
format | Online Article Text |
id | pubmed-8978435 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89784352022-04-05 KMSubtraction: reconstruction of unreported subgroup survival data utilizing published Kaplan-Meier survival curves Zhao, Joseph J. Syn, Nicholas L. Tan, Benjamin Kye Jyn Yap, Dominic Wei Ting Teo, Chong Boon Chan, Yiong Huak Sundar, Raghav BMC Med Res Methodol Research BACKGROUND: Data from certain subgroups of clinical interest may not be presented in primary manuscripts or conference abstract presentations. In an effort to enable secondary data analyses, we propose a workflow to retrieve unreported subgroup survival data from published Kaplan-Meier (KM) plots. METHODS: We developed KMSubtraction, an R-package that retrieves patients from unreported subgroups by matching participants on KM plots of the overall cohort to participants on KM plots of a known subgroup with follow-up time. By excluding matched patients, the opposing unreported subgroup may be retrieved. Reproducibility and limits of error of the KMSubtraction workflow were assessed by comparing unmatched patients against the original survival data of subgroups from published datasets and simulations. Monte Carlo simulations were utilized to evaluate the limits of error of KMSubtraction. RESULTS: The validation exercise found no material systematic error and demonstrates the robustness of KMSubtraction in deriving unreported subgroup survival data. Limits of error were small and negligible on marginal Cox proportional hazard models comparing reconstructed and original survival data of unreported subgroups. Extensive Monte Carlo simulations demonstrate that datasets with high reported subgroup proportion (r = 0.467, p < 0.001), small dataset size (r = − 0.374, p < 0.001) and high proportion of missing data in the unreported subgroup (r = 0.553, p < 0.001) were associated with uncertainty are likely to yield high limits of error with KMSubtraction. CONCLUSION: KMSubtraction demonstrates robustness in deriving survival data from unreported subgroups. The limits of error of KMSubtraction derived from converged Monte Carlo simulations may guide the interpretation of reconstructed survival data of unreported subgroups. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01567-z. BioMed Central 2022-04-03 /pmc/articles/PMC8978435/ /pubmed/35369867 http://dx.doi.org/10.1186/s12874-022-01567-z 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 Zhao, Joseph J. Syn, Nicholas L. Tan, Benjamin Kye Jyn Yap, Dominic Wei Ting Teo, Chong Boon Chan, Yiong Huak Sundar, Raghav KMSubtraction: reconstruction of unreported subgroup survival data utilizing published Kaplan-Meier survival curves |
title | KMSubtraction: reconstruction of unreported subgroup survival data utilizing published Kaplan-Meier survival curves |
title_full | KMSubtraction: reconstruction of unreported subgroup survival data utilizing published Kaplan-Meier survival curves |
title_fullStr | KMSubtraction: reconstruction of unreported subgroup survival data utilizing published Kaplan-Meier survival curves |
title_full_unstemmed | KMSubtraction: reconstruction of unreported subgroup survival data utilizing published Kaplan-Meier survival curves |
title_short | KMSubtraction: reconstruction of unreported subgroup survival data utilizing published Kaplan-Meier survival curves |
title_sort | kmsubtraction: reconstruction of unreported subgroup survival data utilizing published kaplan-meier survival curves |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978435/ https://www.ncbi.nlm.nih.gov/pubmed/35369867 http://dx.doi.org/10.1186/s12874-022-01567-z |
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