Application of credibility ceilings probes the robustness of meta-analyses of biomarkers and cancer risk

J Clin Epidemiol. 2015 Feb;68(2):163-74. doi: 10.1016/j.jclinepi.2014.09.004. Epub 2014 Nov 26.

Abstract

Objectives: Meta-analyses of biomarkers often present spurious significant results and large effects. We applied sensitivity analyses with the use of credibility ceilings to assess whether and how the results of meta-analyses of biomarkers and cancer risk would change.

Study design and setting: We evaluated 98 meta-analyses, 43 (44%) of which had nominally statistically significant results. We assumed that any single study cannot give more than a maximum certainty 100 - c% (c, credibility ceiling) that the effect estimate [odds ratio (OR)] exceeds 1 (null) or 1.2.

Results: Nominal statistical significance was maintained for 21 (21%) meta-analyses, for c = 10% and OR >1, and these proportions changed to 7%, 3%, and 6% with ceilings of 20%, 30%, and 40%, respectively. For ceilings for OR >1.2, the respective proportions were 37%, 21%, 7%, and 3%. Seven meta-analyses on infectious agents retained statistical significance even with a high ceiling of c = 20% for OR >1.00. Meta-analyses without other hints of bias (large between-study heterogeneity, small-study effects, excess significance) were more likely to retain statistical significance than those that had such hints of bias.

Conclusion: Credibility ceilings may be helpful in meta-analyses of biomarkers to understand the robustness of the results to different levels of uncertainty.

Keywords: Biomarkers; Cancer; Credibility ceiling; Meta-analyses; Predictive intervals.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bias*
  • Biomarkers, Tumor / analysis*
  • Clinical Trials as Topic / standards
  • Confounding Factors, Epidemiologic
  • Data Interpretation, Statistical*
  • Humans
  • Meta-Analysis as Topic*
  • Neoplasms / chemistry*
  • Neoplasms / epidemiology
  • Odds Ratio
  • Research Design
  • Risk Factors
  • Selection Bias*

Substances

  • Biomarkers, Tumor