White Ian R, Thomas James
MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK.
Clin Trials. 2005;2(2):141-51. doi: 10.1191/1740774505cn081oa.
The magnitude of the effect of an intervention on a quantitative outcome may be expressed as a standardized mean difference by dividing the difference in means by the standard deviation of the outcome. This is useful to compare outcomes measured using different scales, especially in meta-analysis. However, uncertainty about the standard deviation leads to complicated formulae to avoid bias and to compute the correct standard error. We review approximate and exact formulae and argue for the use of the exact formulae. We then extend the formulae to cluster-randomized trials, and show how the calculations may be implemented using published results. We also describe methods for estimating the standard deviation. Various pitfalls are identified which can lead to major errors especially in the cluster-randomized setting.
一项干预措施对定量结果的影响大小可以通过将均值差异除以结果的标准差来表示为标准化均值差异。这对于比较使用不同量表测量的结果很有用，尤其是在荟萃分析中。然而，标准差的不确定性导致了复杂的公式，以避免偏差并计算正确的标准误差。我们回顾了近似公式和精确公式，并主张使用精确公式。然后我们将这些公式扩展到整群随机试验，并展示如何使用已发表的结果来进行计算。我们还描述了估计标准差的方法。识别出了各种可能导致重大错误的陷阱，尤其是在整群随机试验的情况下。
