Department of Emergency Medicine, Harbor-UCLA Medical Center, 1000 West Carson Street, Torrance, CA 90509, USA.
Ann Emerg Med. 2010 Jun;55(6):544-552.e3. doi: 10.1016/j.annemergmed.2010.01.002.
Subgroup analyses examine associations (eg, between treatment and outcome) within subsets of a larger study sample. The traditional approach evaluates the data in each of the subgroups independently. More accurate answers, however, may be expected when the rest of the data are considered in the analysis of each subgroup, provided there are 3 or more subgroups.
We present a conceptual introduction to subgroup analysis that makes use of all the available data and then illustrate the technique by applying it to a previously published study of pediatric airway management. Using WinBUGS, freely available computer software, we perform an empirical Bayesian analysis of the treatment effect in each of the subgroups. This approach corrects the original subgroup treatment estimates toward a weighted average treatment effect across all subjects.
The revised estimates of the subgroup treatment effects demonstrate markedly less variability than the original estimates. Further, using these estimates will reduce our total expected error in parameter estimation compared with using the original, independent subgroup estimates. Although any particular estimate may be adjusted inappropriately, adopting this strategy will, on average, lead to results that are more accurate.
When multiple subgroups are considered, it is often inadvisable to ignore the rest of the study data. Authors or readers who wish to examine associations within subgroups are encouraged to use techniques that reduce the total expected error.
亚组分析检查了较大研究样本中各亚组内的关联（例如，治疗与结果之间的关联）。传统方法独立评估每个亚组中的数据。然而，如果在分析每个亚组时考虑其余数据，则可能会得到更准确的答案，前提是有 3 个或更多亚组。
我们介绍了一种利用所有可用数据的亚组分析概念性方法，然后通过将其应用于先前发表的儿科气道管理研究来说明该技术。我们使用免费的计算机软件 WinBUGS 在每个亚组中对治疗效果进行经验贝叶斯分析。这种方法会纠正原始亚组治疗估计值，使其朝着所有受试者的加权平均治疗效果靠拢。
与原始估计值相比，修订后的亚组治疗效果估计值的变异性明显降低。此外，与使用原始独立亚组估计值相比，使用这些估计值将减少我们在参数估计中的总预期误差。尽管任何特定的估计值都可能被不恰当地调整，但采用这种策略平均而言会得出更准确的结果。
当考虑多个亚组时，忽略其余研究数据通常是不明智的。鼓励希望检查亚组内关联的作者或读者使用可以降低总预期误差的技术。
