Using median survival in meta-analysis of experimental time-to-event data

Abstract 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, po...

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Autores principales:	Theodore C. Hirst, Emily S. Sena, Malcolm R. Macleod
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Systematic review Meta-analysis Median survival Time-to-event Experimental studies Medicine R
Acceso en línea:	https://doaj.org/article/490e1b6ce0e348c1b15b5742ec7e035a
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spelling	oai:doaj.org-article:490e1b6ce0e348c1b15b5742ec7e035a2021-11-07T12:08:10ZUsing median survival in meta-analysis of experimental time-to-event data10.1186/s13643-021-01824-02046-4053https://doaj.org/article/490e1b6ce0e348c1b15b5742ec7e035a2021-11-01T00:00:00Zhttps://doi.org/10.1186/s13643-021-01824-0https://doaj.org/toc/2046-4053Abstract 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.Theodore C. HirstEmily S. SenaMalcolm R. MacleodBMCarticleSystematic reviewMeta-analysisMedian survivalTime-to-eventExperimental studiesMedicineRENSystematic Reviews, Vol 10, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Systematic review Meta-analysis Median survival Time-to-event Experimental studies Medicine R
spellingShingle	Systematic review Meta-analysis Median survival Time-to-event Experimental studies Medicine R Theodore C. Hirst Emily S. Sena Malcolm R. Macleod Using median survival in meta-analysis of experimental time-to-event data
description	Abstract 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.
format	article
author	Theodore C. Hirst Emily S. Sena Malcolm R. Macleod
author_facet	Theodore C. Hirst Emily S. Sena Malcolm R. Macleod
author_sort	Theodore C. Hirst
title	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_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_sort	using median survival in meta-analysis of experimental time-to-event data
publisher	BMC
publishDate	2021
url	https://doaj.org/article/490e1b6ce0e348c1b15b5742ec7e035a
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Using median survival in meta-analysis of experimental time-to-event data

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