Abstract
BackgroundTime-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.MethodsWe 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.ResultsThere 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.ConclusionsWe 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.
Highlights
Many types of biomedical research use time-to-event data—this is prominent in cancer research which rightly attracts considerable focus given the significant economic and health burden that it inflicts on society throughout the world [1, 2].Meta-analysis is a tool to combine data from multiple studies in order to increase the precision of an estimation of treatment effect
Michiels et al [23] compared meta-analyses of clinical trial data using individual patient data (IPD)-derived hazard ratios (HRs), an odds ratio-based approach, and median survival ratio (MSR) as a summary statistic. They found comparable global estimates of effect for each method, and on this basis, we developed a technique for pooling survival data using median survival ratio, with the number of animals per experiment used for weighting in place of inverse variance [24]
Meta‐analysis and meta‐regression sensitivity assessment we looked to examine the performance of each summary statistic on application to meta-analysis
Summary
Meta-analysis is a tool to combine data from multiple studies in order to increase the precision of an estimation of treatment effect. It is used commonly in clinical practice to combine data from large clinical trials and is as such heralded among the highest levels of evidence in medicine [3, 4]. Collinearity is often present in preclinical survival datasets, so multivariate meta-regression has a theoretical advantage in identifying and accounting for interactions between predictors. This is thought to come at the cost of further compromise in sensitivity. We aimed to appraise its performance when compared with hazard ratio-based meta-analysis when pooling large numbers of small, imprecise studies
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