Clinical Trials and Evidence-Based Medicine Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Int J Epidemiol. 2011 Oct;40(5):1292-307. doi: 10.1093/ije/dyr099. Epub 2011 Jul 6.
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.
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).
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.
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.
大型研究可能会发现假设的风险因素和干预措施，其效果非常小。我们旨在通过实证评估大量具有微小统计学意义的相对风险（RR）及其研究者的解释。
在队列研究文章的摘要中确定 RR 在 0.95 到 1.05 之间；在《新英格兰医学杂志》、《美国医学会杂志》或《柳叶刀》上发表的文章；以及 Cochrane 综述。对于每个符合条件的微小效果及其相应的研究，我们记录了研究设计、参与者、风险因素/干预措施、结果、效应估计、P 值和研究人员的解释等信息。我们还计算了每个效应落在特定零假设区间（RR 区间 0.97-1.03、0.95-1.05、0.90-1.10）之外的概率。
我们评估了 51 个符合条件的微小效果（风险因素的中位数样本量为 112786，干预措施的中位数样本量为 36021）。大多数（37/51）出现在 2006-10 年发表的文章中。这些效果涉及营养（n=19）、遗传和其他生物标志物（n=8）、卫生保健相关因素（n=8）和其他各种具有临床或公共卫生重要性的主题（n=16），主要涉及主要临床结局。51 个效应中有 15 个大于 80%的可能性落在 RR 区间 0.97-1.03 之外，但只有 8 个大于 40%的可能性落在 RR 区间 0.95-1.05 之外，没有一个大于 1.7%的可能性落在 RR 区间 0.90-1.10 之外。作者对 23 个效应（小幅度 n=19、残留混杂 n=11、选择偏差 n=1）至少讨论了一个关注问题。对 28 个效应没有表达关注。
在文献中，具有临床或公共卫生重要性的风险因素和干预措施的统计学上显著微小效应变得越来越常见。需要谨慎解释，因为即使是最小的偏倚也可以消除这些效应中的大多数，并且其重要性不确定。
