Risk factors and interventions with statistically significant tiny effects

• Published in: International journal of epidemiology 2011-10, Vol.40 (5), p.1292-1307
• Main Authors: Siontis, George CM, Ioannidis, John PA
• Format: Article
• Language: English
• Subjects: Bias; Biological and medical sciences; Confidence Intervals; Data Interpretation, Statistical; Humans; Medical sciences; Meta-Analysis as Topic; Miscellaneous; Public health. Hygiene; Public health. Hygiene-occupational medicine; Risk; Risk Factors; Treatment Outcome
• ISSN: 0300-5771; 1464-3685
• DOI: 10.1093/ije/dyr099

Background Large studies may identify postulated risk factors and interventions with very small effect sizes. We aimed to assess empirically a large number of statistically significant relative risks (RRs) of tiny magnitude and their interpretation by investigators. Methods RRs in the range between 0.95 and 1.05 were identified in abstracts of articles of cohort studies; articles published in NEJM, JAMA or Lancet; and Cochrane reviews. For each eligible tiny effect and the respective study, we recorded information on study design, participants, risk factor/intervention, outcome, effect estimates, P-values and interpretation by study investigators. We also calculated the probability that each effect lies outside specific intervals around the null (RR interval 0.97-1.03, 0.95-1.05, 0.90-1.10). Results We evaluated 51 eligible tiny effects (median sample size 112 786 for risk factors and 36 021 for interventions). Most (37/51) appeared in articles published in 2006-10. The effects pertained to nutrition (n = 19), genetic and other biomarkers (n = 8), correlates of health care (n = 8) and diverse other topics (n = 16) of clinical or public health importance and mostly referred to major clinical outcomes. A total of 15 of the 51 effects were >80% likely to lie outside the RR interval 0.97-1.03, but only 8 were >40% likely to lie outside the RR interval 0.95-1.05 and none was >1.7% likely to lie outside the RR interval 0.90-1.10. The authors discussed at least one concern for 23 effects (small magnitude n = 19, residual confounding n = 11, selection bias n = 1). No concerns were expressed for 28 effects. Conclusions Statistically significant tiny effects for risk factors and interventions of clinical or public health importance become more common in the literature. Cautious interpretation is warranted, since most of these effects could be eliminated with even minimal biases and their importance is uncertain.