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Abstract：
Aim This study aims to establish the test sensitivity and specificity of the I2-point estimate for testing selection bias in meta-analyses under the condition of large versus small trial sample size and large versus small trial number in meta-analyses and to test the null hypotheses that the differences are not statistically significant. Material and methods Simulation trials were generated in MS Excel (Microsoft Corp., Redmond, WA), each consisting of a sequence of subject ID (accession) numbers representing trial subjects, a random sequence of allocation to group A or B, and a random sequence of a simulated baseline variable (\"age\") per subject, ranging from 50 to 55. These simulation trials were included in five types of meta-analyses with large/small numbers of trials, as well as trials with large and small sample sizes. Half of the meta-analyses were artificially biased. All meta-analyses were tested using the I2-point estimate. The numbers of true positive (TP), false positive (FP), false negative (FN), and true negative (TN) test results were established. From these, the test sensitivity and specificity were computed for each of the meta-analysis types and compared. Results All non-biased meta-analyses yielded true negative, and all biased meta-analyses yielded true positive test results, regardless of trial number and trial sample size. No false positive or false negative test results were observed. Accordingly, test sensitivities and specificities of 100% for all meta-analysis types were established, and thus, both null hypotheses failed to be rejected. Conclusion The results suggest that trial number and sample size in a baseline variable meta-analysis do not affect the test accuracy of the I2-point estimate.
摘要：
目的本研究旨在建立I2点估计值的测试灵敏度和特异性，以在荟萃分析中在大与小试验样本量和大与小试验数量的条件下测试荟萃分析中的选择偏倚，并测试差异无统计学意义的假设。材料和方法模拟试验在MSExcel中生成(MicrosoftCorp.,雷德蒙德,WA),每个由代表试验受试者的受试者ID（加入）编号序列组成，分配给A组或B组的随机序列，和每个受试者的模拟基线变量（“年龄”）的随机序列，从50到55。这些模拟试验包括在五种类型的荟萃分析中，有大量/少量的试验，以及大样本和小样本的试验。一半的荟萃分析是人为偏差的。所有荟萃分析均使用I2点估计值进行测试。真阳性（TP）的数量，假阳性(FP)，假阴性（FN），并建立了真阴性（TN）测试结果。从这些,我们计算了每种meta分析类型的检测灵敏度和特异度,并进行了比较.结果所有无偏荟萃分析均为真阴性，所有有偏见的荟萃分析都产生了真正的阳性测试结果，不考虑试验数量和试验样本量。没有观察到假阳性或假阴性测试结果。因此，建立了所有荟萃分析类型100%的测试敏感性和特异性，因此，两个零假设都没有被拒绝。结论结果表明，基线变量荟萃分析中的试验数量和样本量不会影响I2点估计值的检验准确性。
