School of Pharmacy, The University of Queensland, Brisbane, Australia.
J Clin Pharm Ther. 2011 Dec;36(6):637-41. doi: 10.1111/j.1365-2710.2010.01222.x. Epub 2010 Dec 12.
The importance of statistical power is widely recognized from a pre-trial perspective, and when interpreting results that are not statistically significant. It is less well recognized that poor power can lead to inflated estimates of the effect size when statistically significant results are observed. We use trial simulations to quantify this bias, which we term 'significant-result bias'.
Significant-result bias is explained, and simulations are used to estimate possible significant-result bias in the rate of thrombotic events observed in the APPROVe trial. Statistically significant results, on outcomes for which there is empirical evidence of poor power, may provide inflated estimates of the size of effect.
If independent evidence is available to judge the likely effect size of an underpowered statistical test, trial simulations can provide a method for quantifying significant-result bias.
从审判前的角度来看，统计功效的重要性已得到广泛认可，并且在解释没有统计学意义的结果时也是如此。人们较少认识到，当观察到具有统计学意义的结果时，低功效也会导致对效应大小的估计过高。我们使用试验模拟来量化这种偏差，我们称之为“显著结果偏差”。
解释了显著结果偏差，并使用模拟来估计 APPROVe 试验中观察到的血栓事件发生率的显著结果偏差的可能性。对于功效较差的结果，如果有经验证据表明统计功效较低，则具有统计学意义的结果可能会提供对效应大小的过高估计。
如果有独立的证据可以判断统计检验功效不足的可能效应大小，则试验模拟可以提供一种量化显著结果偏差的方法。
