Evaluation of Excess Statistical Significance in Meta-analyses of 98 Biomarker Associations with Cancer Risk is a research paper published in JNCI: Journal of the National Cancer Institute (2012). On theSindex it has a DataRank of 3.0. It has been cited 96 times, with 59 citing works in its 1-hop citation network.
BackgroundNumerous biomarkers have been associated with cancer risk. We assessed whether there is evidence for excess statistical significance in results of cancer biomarker studies, suggesting biases.MethodsWe systematically searched PubMed for meta-analyses of nongenetic biomarkers and cancer risk. The number of observed studies with statistically significant results was compared with the expected number, based on the statistical power of each study under different assumptions for the plausible effect size. We also evaluated small-study effects using asymmetry tests. All statistical tests were two-sided.ResultsWe included 98 meta-analyses with 847 studies. Forty-three meta-analyses (44%) found nominally statistically significant summary effects (random effects). The proportion of meta-analyses with statistically significant effects was highest for infectious agents (86%), inflammatory (67%), and insulin-like growth factor (IGF)/insulin system (52%) biomarkers. Overall, 269 (32%) individual studies observed nominally statistically significant results. A statistically significant excess of the observed over the expected number of studies with statistically significant results was seen in 20 meta-analyses. An excess of observed vs expected was observed in studies of IGF/insulin (P ≤ .04) and inflammation systems (P ≤ .02). Only 12 meta-analyses (12%) had a statistically significant summary effect size, more than 1000 case patients, and no hints of small-study effects or excess statistical significance; only four of them had large effect sizes, three of which pertained to infectious agents (Helicobacter pylori, hepatitis and human papilloma viruses).ConclusionsMost well-documented biomarkers of cancer risk without evidence of bias pertain to infectious agents. Conversely, an excess of statistically significant findings was observed in studies of IGF/insulin and inflammation systems, suggesting reporting biases.
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Base Score Contribution
0.686
From this paper's citation signal
Citation Network Contribution
2.3
From 52 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 23% comes from its base citations and 77% from the citation network (52 citing papers contributed measurable signal).
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