Primary study authors of significant studies are more likely to believe that a strong association exists in a heterogeneous meta-analysis compared with methodologists.

OBJECTIVE To assess the interpretation of a highly heterogeneous meta-analysis by authors of primary studies and by methodologists. STUDY DESIGN AND SETTING We surveyed the authors of studies on the association between insulin-like growth factor 1 (IGF-1) and prostate cancer, and 20 meta-analysis methodologists. Authors and methodologists presented with the respective meta-analysis results were queried about the effect size and potential causality of the association. We evaluated whether author responses correlated with the number of IGF-related articles they had published and their study results included in the meta-analysis. We also compared authors' and methodologists' responses. RESULTS Authors who had published more IGF-related papers offered more generous effect size estimates for the association (ρ(s)=0.61, P=0.01) and higher likelihood that the odds ratio (OR) was greater than 1.20 (ρ(s)=0.63, P=0.01). Authors who had published themselves studies with statistically significant effects for a positive association were more likely to believe that the true OR is greater than 1.20 compared with methodologists (median likelihood 50% versus 2.5%, P=0.01). CONCLUSION Researchers are influenced by their own investment in the field, when interpreting a meta-analysis that includes their own study. Authors who published significant results are more likely to believe that a strong association exists compared with methodologists.

[1]  T. Greenhalgh,et al.  Seeing what you want to see in randomised controlled trials: versions and perversions of UKPDS data , 2000, BMJ : British Medical Journal.

[2]  J. Ioannidis,et al.  Relative Citation Impact of Various Study Designs in the Health Sciences , 2005, JAMA.

[3]  A. Hrõbjartsson,et al.  Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. , 2004, JAMA.

[4]  Meir J. Stampfer,et al.  Plasma Insulin-Like Growth Factor-I and Prostate Cancer Risk: A Prospective Study , 1998 .

[5]  I. Olkin,et al.  Meta-analysis of observational studies in epidemiology - A proposal for reporting , 2000 .

[6]  H. Markovits,et al.  The belief-bias effect in the production and evaluation of logical conclusions , 1989, Memory & cognition.

[7]  K. Dickersin,et al.  Publication Bias: The Problem That Won't Go Away , 1993, Annals of the New York Academy of Sciences.

[8]  J. Ioannidis,et al.  Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias , 2008, PloS one.

[9]  B. Bausell,et al.  REVIEWING THE REVIEWS , 2001, International Journal of Technology Assessment in Health Care.

[10]  D. Sackett,et al.  Evidence based medicine: what it is and what it isn't , 1996, BMJ.

[11]  E. Wynder,et al.  The wish bias. , 1990, Journal of clinical epidemiology.

[12]  Xitao Fan,et al.  Comparing response rates in e-mail and paper surveys: A meta-analysis , 2009 .

[13]  Ross J. Harris,et al.  Circulating insulin‐like growth factor peptides and prostate cancer risk: A systematic review and meta‐analysis , 2009, International journal of cancer.

[14]  R. Shi,et al.  Insulin-like growth factor-I and prostate cancer: a meta-analysis , 2001, British Journal of Cancer.

[15]  D. Sackett Applying overviews and meta-analyses at the bedside. , 1995, Journal of clinical epidemiology.

[16]  Richard D Riley,et al.  Interpretation of random effects meta-analyses , 2011, BMJ : British Medical Journal.

[17]  John P A Ioannidis,et al.  Effect of formal statistical significance on the credibility of observational associations. , 2008, American journal of epidemiology.

[18]  R Brian Haynes,et al.  Evidence based medicine: what it is and what it isn't. 1996. , 2007, Clinical orthopaedics and related research.

[19]  T. Kaptchuk,et al.  Effect of interpretive bias on research evidence. , 2003, BMJ.

[20]  John P A Ioannidis,et al.  Reasons or excuses for avoiding meta-analysis in forest plots , 2008, BMJ : British Medical Journal.

[21]  D. Altman,et al.  Measuring inconsistency in meta-analyses , 2003, BMJ : British Medical Journal.

[22]  David Moher,et al.  Meta-analysis of Observational Studies in Epidemiology , 2000 .

[23]  J. Koehler The Influence of Prior Beliefs on Scientific Judgments of Evidence Quality , 1993 .

[24]  S. Thompson,et al.  Quantifying heterogeneity in a meta‐analysis , 2002, Statistics in medicine.

[25]  J. Vandenbroucke,et al.  175th anniversary lecture. Medical journals and the shaping of medical knowledge. , 1998, The Lancet.

[26]  G. Guyatt,et al.  Going from evidence to recommendations , 2008, BMJ : British Medical Journal.

[27]  M. Clarke,et al.  Increasing response rates to postal questionnaires: systematic review , 2002, BMJ : British Medical Journal.

[28]  Matthias Egger,et al.  Insulin-like growth factor (IGF)-I, IGF binding protein-3, and cancer risk: systematic review and meta-regression analysis , 2004, The Lancet.

[29]  François G. Meyer,et al.  Insulin-like Growth Factors, Their Binding Proteins, and Prostate Cancer Risk: Analysis of Individual Patient Data from 12 Prospective Studies , 2008, Annals of Internal Medicine.

[30]  J. Ioannidis,et al.  The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. , 2009, Journal of clinical epidemiology.

[31]  John P A Ioannidis,et al.  Meta‐research: The art of getting it wrong , 2010, Research synthesis methods.

[32]  Douglas G Altman,et al.  Reporting and interpretation of randomized controlled trials with statistically nonsignificant results for primary outcomes. , 2010, JAMA.

[33]  E. Regidor Subjective judgements in scientific practice and art , 2022 .

[34]  D. English,et al.  Circulating Insulin-Like Growth Factor-I and Binding Protein-3 and Risk of Prostate Cancer , 2006, Cancer Epidemiology Biomarkers & Prevention.

[35]  Alan Cantor,et al.  The uncertainty principle and industry-sponsored research , 2000, The Lancet.

[36]  C. Mantzoros,et al.  Insulin-like growth factor 1 in relation to prostate cancer and benign prostatic hyperplasia. , 1997, British Journal of Cancer.

[37]  J. Ioannidis Why Most Discovered True Associations Are Inflated , 2008, Epidemiology.

[38]  J. Ioannidis,et al.  Industry sponsorship and selection of comparators in randomized clinical trials , 2010, European journal of clinical investigation.

[39]  C. Mantzoros,et al.  Hormonal predictors of prostate cancer: a meta-analysis. , 2000, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[40]  J. Neilson,et al.  Statistical methods can be improved within Cochrane pregnancy and childbirth reviews. , 2011, Journal of clinical epidemiology.

[41]  B. Margetts United Kingdom prospective diabetes study , 1995, BMJ.

[42]  Chris Todd,et al.  Not another questionnaire! Maximizing the response rate, predicting non-response and assessing non-response bias in postal questionnaire studies of GPs. , 2002, Family practice.

[43]  K. Dickersin,et al.  Factors influencing publication of research results. Follow-up of applications submitted to two institutional review boards. , 1992, JAMA.

[44]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement , 2009, BMJ.

[45]  George Liberopoulos,et al.  Selection in Reported Epidemiological Risks: An Empirical Assessment , 2007, PLoS medicine.

[46]  G. Guyatt,et al.  GRADE: an emerging consensus on rating quality of evidence and strength of recommendations , 2008, BMJ : British Medical Journal.

[47]  Andrea Furlan,et al.  The interpretation of systematic reviews with meta-analyses: an objective or subjective process? , 2008, BMC Medical Informatics Decis. Mak..

[48]  Gordon H Guyatt,et al.  Systems for grading the quality of evidence and the strength of recommendations II: Pilot study of a new system , 2005, BMC health services research.

[49]  David Moher,et al.  Non-Cochrane vs. Cochrane reviews were twice as likely to have positive conclusion statements: cross-sectional study. , 2009, Journal of clinical epidemiology.

[50]  N. Wald,et al.  Insulin-like growth factors and cancer: no role in screening. Evidence from the BUPA study and meta-analysis of prospective epidemiological studies , 2006, British Journal of Cancer.

[51]  John P A Ioannidis,et al.  Statistically significant meta-analyses of clinical trials have modest credibility and inflated effects. , 2011, Journal of clinical epidemiology.

[52]  K. Dickersin How important is publication bias? A synthesis of available data. , 1997, AIDS education and prevention : official publication of the International Society for AIDS Education.

[53]  R. MacCoun,et al.  Biases in the interpretation and use of research results. , 1998, Annual review of psychology.

[54]  D. Altman,et al.  Outcome reporting bias in randomized trials funded by the Canadian Institutes of Health Research , 2004, Canadian Medical Association Journal.