Older studies can underestimate prognosis of glioblastoma biomarker in meta-analyses: a meta-epidemiological study for study-level effect in the current literature.

INTRODUCTION: There are many potential biomarkers in glioblastoma (GBM), and meta-analyses represent the highest level of evidence when inferring their prognostic significance. It is possible however, that inherent design properties of the studies included in these meta-analyses may affect the pooled hazard ratio (HR) of the meta-analyses. This meta-epidemiological study aims to investigate the potential bias of three study-level properties in meta-analyses of GBM biomarkers currently published in the literature.

METHODS: Seven electronic databases from inception to December 2017 were searched for meta-analyses evaluating different GBM biomarkers, which were screened against specific criteria. Study-level data were extracted from each meta-analysis, and analyzed using logistic regression to yield the ratio of HR (RHR) summary statistic.

RESULTS: Nine meta-analyses investigating different GBM biomarkers were included. Of all the meta-analyses, the HRs of two studies were significantly underestimated by older studies; they investigated biomarkers IDH1 (RHR = 1.145; p = 0.017) and CD133 (RHR = 0.850; p = 0.013). Study-level size and design showed non-significant trends towards affecting the overall HR in all included meta-analyses.

CONCLUSIONS: This meta-epidemiological study demonstrated that study-level year can already significantly affect the pooled HR of GBM biomarkers reported by meta-analyses. It is possible that in the future, more study-level properties will exert significant effect. In terms of future GBM biomarker meta-analyses, special consideration of bias should be given to these study-level properties as potential sources of effect on the prognostic pooled HR.