Radboud Institute for Health Sciences (RIHS), Radboud University Medical Center, Mailbox 133, P.O. Box 9101, Nijmegen 6500 HB, The Netherlands.
Stanford Prevention Research Center, Department of Medicine, Stanford University School of Humanities and Sciences, 1265 Welch Road, Stanford, CA 94305, USA; Department of Health Research and Policy, Stanford University School of Medicine, 150 Governor's Lane, Stanford, CA 94305, USA; Department of Statistics, Stanford University School of Humanities and Sciences, 390 Serra Mall, Stanford, CA 94305, USA; Meta-Research Innovation Center at Stanford (METRICS), Stanford University, 1265 Welch Road, Stanford, CA 94305, USA.
J Clin Epidemiol. 2015 Aug;68(8):860-9. doi: 10.1016/j.jclinepi.2015.03.017. Epub 2015 Apr 2.
Between-study heterogeneity plays an important role in random-effects models for meta-analysis. Most clinical trials are small, and small trials are often associated with larger effect sizes. We empirically evaluated whether there is also a relationship between trial size and heterogeneity (τ).
We selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009-2013 with a dichotomous (n = 2,009) or continuous (n = 1,254) outcome. The association between estimated τ and trial size was evaluated across meta-analyses using regression and within meta-analyses using a Bayesian approach. Small trials were predefined as those having standard errors (SEs) over 0.2 standardized effects.
Most meta-analyses were based on few (median 4) trials. Within the same meta-analysis, the small study τS(2) was larger than the large-study τL(2) [average ratio 2.11; 95% credible interval (1.05, 3.87) for dichotomous and 3.11 (2.00, 4.78) for continuous meta-analyses]. The imprecision of τS was larger than of τL: median SE 0.39 vs. 0.20 for dichotomous and 0.22 vs. 0.13 for continuous small-study and large-study meta-analyses.
Heterogeneity between small studies is larger than between larger studies. The large imprecision with which τ is estimated in a typical small-studies' meta-analysis is another reason for concern, and sensitivity analyses are recommended.
在荟萃分析的随机效应模型中，研究间异质性起着重要作用。大多数临床试验规模较小，而小试验通常与更大的效应量相关。我们通过实证评估了试验规模与异质性（τ）之间是否也存在关系。
我们选择了 2009 年至 2013 年 Cochrane 系统评价数据库每一期干预性综述中的第一篇荟萃分析，这些荟萃分析的结果为二分类（n = 2009）或连续（n = 1254）结局。我们使用回归在荟萃分析之间评估估计的τ与试验规模之间的关系，并使用贝叶斯方法在荟萃分析内进行评估。小试验被定义为标准误差（SE）超过 0.2 个标准化效应的试验。
大多数荟萃分析基于少数（中位数为 4）试验。在同一荟萃分析中，小样本研究的τ S（2）大于大样本研究的τ L（2）[二分类的平均比值为 2.11；95%可信区间（1.05，3.87）；连续的为 3.11（2.00，4.78）]。τ S的不精确性大于τ L：二分类的中位数 SE 分别为 0.39 和 0.20，小样本和大样本的连续 SE 分别为 0.22 和 0.13。
小研究之间的异质性大于大研究之间的异质性。在典型的小样本荟萃分析中，τ的估计精度不高是另一个令人担忧的原因，建议进行敏感性分析。
