Mickenautsch Steffen, Yengopal Veerasamy
Faculty of Dentistry, University of the Western Cape, Cape Town, ZAF.
Community Dentistry, University of the Witwatersrand, Johannesburg, ZAF.
Cureus. 2025 May 25;17(5):e84769. doi: 10.7759/cureus.84769. eCollection 2025 May.
This technical report demonstrates that the use of the I statistic for testing selection bias in single randomised controlled trials (RCTs) has the potential to allow the prevention of false-positive test results, thereby allowing for high test specificity and a high positive predictive value. In addition, the I statistic provides utility for the in-depth identification of low-level selection bias in RCTs, thus assisting in the avoidance of false-negative test results and possibly for estimating the percentage of trial patients with biased allocation into RCT treatment groups. Future studies to this topic may investigate whether cases with I estimates above 0%, due to chance rather than selection bias, are possible and, if so, how to distinguish such cases from those with very low bias levels. Future studies may also test the null hypothesis that levels of selection bias are not associated with any over- or underestimation of the true effect estimates of RCTs.
本技术报告表明,在单随机对照试验(RCT)中使用I统计量来检验选择偏倚有可能防止出现假阳性检验结果,从而实现高检验特异性和高阳性预测值。此外,I统计量有助于深入识别RCT中的低水平选择偏倚,从而有助于避免假阴性检验结果,并可能用于估计随机分配到RCT治疗组的有偏倚患者的百分比。关于该主题的未来研究可能会探讨是否有可能出现由于机会而非选择偏倚导致I估计值高于0%的情况,如果是这样,如何将这些情况与偏倚水平极低的情况区分开来。未来的研究还可能检验选择偏倚水平与RCT真实效应估计值的任何高估或低估均无关的零假设。