Cargando…
Using median survival in meta-analysis of experimental time-to-event data
BACKGROUND: Time-to-event data is frequently reported in both clinical and preclinical research spheres. Systematic review and meta-analysis is a tool that can help to identify pitfalls in preclinical research conduct and reporting that can help to improve translational efficacy. However, pooling of...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8561932/ https://www.ncbi.nlm.nih.gov/pubmed/34727973 http://dx.doi.org/10.1186/s13643-021-01824-0 |
_version_ | 1784593168301293568 |
---|---|
author | Hirst, Theodore C. Sena, Emily S. Macleod, Malcolm R. |
author_facet | Hirst, Theodore C. Sena, Emily S. Macleod, Malcolm R. |
author_sort | Hirst, Theodore C. |
collection | PubMed |
description | BACKGROUND: Time-to-event data is frequently reported in both clinical and preclinical research spheres. Systematic review and meta-analysis is a tool that can help to identify pitfalls in preclinical research conduct and reporting that can help to improve translational efficacy. However, pooling of studies using hazard ratios (HRs) is cumbersome especially in preclinical meta-analyses including large numbers of small studies. Median survival is a much simpler metric although because of some limitations, which may not apply to preclinical data, it is generally not used in survival meta-analysis. We aimed to appraise its performance when compared with hazard ratio-based meta-analysis when pooling large numbers of small, imprecise studies. METHODS: We simulated a survival dataset with features representative of a typical preclinical survival meta-analysis, including with influence of a treatment and a number of covariates. We calculated individual patient data-based hazard ratios and median survival ratios (MSRs), comparing the summary statistics directly and their performance at random-effects meta-analysis. Finally, we compared their sensitivity to detect associations between treatment and influential covariates at meta-regression. RESULTS: There was an imperfect correlation between MSR and HR, although the opposing direction of treatment effects between summary statistics appeared not to be a major issue. Precision was more conservative for HR than MSR, meaning that estimates of heterogeneity were lower. There was a slight sensitivity advantage for MSR at meta-analysis and meta-regression, although power was low in all circumstances. CONCLUSIONS: We believe we have validated MSR as a summary statistic for use in a meta-analysis of small, imprecise experimental survival studies—helping to increase confidence and efficiency in future reviews in this area. While assessment of study precision and therefore weighting is less reliable, MSR appears to perform favourably during meta-analysis. Sensitivity of meta-regression was low for this set of parameters, so pooling of treatments to increase sample size may be required to ensure confidence in preclinical survival meta-regressions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-021-01824-0. |
format | Online Article Text |
id | pubmed-8561932 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85619322021-11-03 Using median survival in meta-analysis of experimental time-to-event data Hirst, Theodore C. Sena, Emily S. Macleod, Malcolm R. Syst Rev Methodology BACKGROUND: Time-to-event data is frequently reported in both clinical and preclinical research spheres. Systematic review and meta-analysis is a tool that can help to identify pitfalls in preclinical research conduct and reporting that can help to improve translational efficacy. However, pooling of studies using hazard ratios (HRs) is cumbersome especially in preclinical meta-analyses including large numbers of small studies. Median survival is a much simpler metric although because of some limitations, which may not apply to preclinical data, it is generally not used in survival meta-analysis. We aimed to appraise its performance when compared with hazard ratio-based meta-analysis when pooling large numbers of small, imprecise studies. METHODS: We simulated a survival dataset with features representative of a typical preclinical survival meta-analysis, including with influence of a treatment and a number of covariates. We calculated individual patient data-based hazard ratios and median survival ratios (MSRs), comparing the summary statistics directly and their performance at random-effects meta-analysis. Finally, we compared their sensitivity to detect associations between treatment and influential covariates at meta-regression. RESULTS: There was an imperfect correlation between MSR and HR, although the opposing direction of treatment effects between summary statistics appeared not to be a major issue. Precision was more conservative for HR than MSR, meaning that estimates of heterogeneity were lower. There was a slight sensitivity advantage for MSR at meta-analysis and meta-regression, although power was low in all circumstances. CONCLUSIONS: We believe we have validated MSR as a summary statistic for use in a meta-analysis of small, imprecise experimental survival studies—helping to increase confidence and efficiency in future reviews in this area. While assessment of study precision and therefore weighting is less reliable, MSR appears to perform favourably during meta-analysis. Sensitivity of meta-regression was low for this set of parameters, so pooling of treatments to increase sample size may be required to ensure confidence in preclinical survival meta-regressions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13643-021-01824-0. BioMed Central 2021-11-02 /pmc/articles/PMC8561932/ /pubmed/34727973 http://dx.doi.org/10.1186/s13643-021-01824-0 Text en © The Author(s) 2021 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 | Methodology Hirst, Theodore C. Sena, Emily S. Macleod, Malcolm R. Using median survival in meta-analysis of experimental time-to-event data |
title | Using median survival in meta-analysis of experimental time-to-event data |
title_full | Using median survival in meta-analysis of experimental time-to-event data |
title_fullStr | Using median survival in meta-analysis of experimental time-to-event data |
title_full_unstemmed | Using median survival in meta-analysis of experimental time-to-event data |
title_short | Using median survival in meta-analysis of experimental time-to-event data |
title_sort | using median survival in meta-analysis of experimental time-to-event data |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8561932/ https://www.ncbi.nlm.nih.gov/pubmed/34727973 http://dx.doi.org/10.1186/s13643-021-01824-0 |
work_keys_str_mv | AT hirsttheodorec usingmediansurvivalinmetaanalysisofexperimentaltimetoeventdata AT senaemilys usingmediansurvivalinmetaanalysisofexperimentaltimetoeventdata AT macleodmalcolmr usingmediansurvivalinmetaanalysisofexperimentaltimetoeventdata |