Evaluation of Excess Statistical Significance in Meta-analyses of 98 Biomarker Associations with Cancer Risk
Journal
JNCI Journal of the National Cancer Institute
Date Issued
October 22, 2012
DOI
10.1093/jnci/djs437
Abstract
Backround
Numerous 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.
Methods
We 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.
Results
We 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).
Conclusions
Most 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.
Numerous 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.
Methods
We 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.
Results
We 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).
Conclusions
Most 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.