Application of credibility ceilings probes the robustness of meta-analyses of biomarkers and cancer risk is a research paper published in Journal of Clinical Epidemiology (2015). On theSindex it has a DataRank of 0.524. It has been cited 32 times.
ObjectivesMeta-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 settingWe 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.ResultsNominal 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.ConclusionCredibility ceilings may be helpful in meta-analyses of biomarkers to understand the robustness of the results to different levels of uncertainty.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.524
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.