BACKGROUND: 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.
BACKGROUND: 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 humanpapilloma 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.
Authors: Melissa A Merritt; Ioanna Tzoulaki; Shelley S Tworoger; Immaculata De Vivo; Susan E Hankinson; Judy Fernandes; Konstantinos K Tsilidis; Elisabete Weiderpass; Anne Tjønneland; Kristina E N Petersen; Christina C Dahm; Kim Overvad; Laure Dossus; Marie-Christine Boutron-Ruault; Guy Fagherazzi; Renée T Fortner; Rudolf Kaaks; Krasimira Aleksandrova; Heiner Boeing; Antonia Trichopoulou; Christina Bamia; Dimitrios Trichopoulos; Domenico Palli; Sara Grioni; Rosario Tumino; Carlotta Sacerdote; Amalia Mattiello; H Bas Bueno-de-Mesquita; N Charlotte Onland-Moret; Petra H Peeters; Inger T Gram; Guri Skeie; J Ramón Quirós; Eric J Duell; María-José Sánchez; D Salmerón; Aurelio Barricarte; Saioa Chamosa; Ulrica Ericson; Emily Sonestedt; Lena Maria Nilsson; Annika Idahl; Kay-Tee Khaw; Nicholas Wareham; Ruth C Travis; Sabina Rinaldi; Isabelle Romieu; Chirag J Patel; Elio Riboli; Marc J Gunter Journal: Cancer Epidemiol Biomarkers Prev Date: 2015-02 Impact factor: 4.254
Authors: Katherine S Button; John P A Ioannidis; Claire Mokrysz; Brian A Nosek; Jonathan Flint; Emma S J Robinson; Marcus R Munafò Journal: Nat Rev Neurosci Date: 2013-04-10 Impact factor: 34.870
Authors: Artemisia Kakourou; Charalampia Koutsioumpa; David S Lopez; Judith Hoffman-Bolton; Gary Bradwin; Nader Rifai; Kathy J Helzlsouer; Elizabeth A Platz; Konstantinos K Tsilidis Journal: Cancer Causes Control Date: 2015-07-29 Impact factor: 2.506
Authors: Akiko Hanyuda; Shuji Ogino; Zhi Rong Qian; Reiko Nishihara; Mingyang Song; Kosuke Mima; Kentaro Inamura; Yohei Masugi; Kana Wu; Jeffrey A Meyerhardt; Andrew T Chan; Charles S Fuchs; Edward L Giovannucci; Yin Cao Journal: Int J Cancer Date: 2016-05-10 Impact factor: 7.396