Basle Institute for Clinical Epidemiology and Biostatistics, University of Basle, Basle, Switzerland.
Department of Surgery, University of Basle, Basle, Switzerland.
Br J Surg. 2018 Feb;105(3):182-191. doi: 10.1002/bjs.10763.
Multiple surgical procedures in a single patient are relatively common and lead to dependent (clustered) data. This dependency needs to be accounted for in study design and data analysis. A systematic review was performed to assess how clustered data were handled in inguinal hernia trials. The impact of ignoring clustered data was estimated using simulations.
PubMed, Embase and the Cochrane Library were reviewed systematically for RCTs published between 2004 and 2013, including patients undergoing unilateral or bilateral inguinal hernia repair. Study characteristics determining the appropriateness of handling clustered data were extracted. Using simulations, various statistical methods accounting for clustered data were compared with an analysis ignoring clustering by assuming 100 hernias, with a varying percentage of patients having bilateral hernias.
Of the 50 eligible trials including patients with bilateral hernias, 20 (40 per cent) did not provide information on how they dealt with clustered data and 18 (36 per cent) avoided clustering by assessing the outcome by patient and not by hernia. None of the remaining 12 trials (24 per cent) considered clustering in the design or analysis. In the simulations, ignoring clustering led to an increased type I error rate of up to 12 per cent and to a loss in power of up to 15 per cent, depending on whether the patient or the hernia was the randomization unit.
Clustering was rarely considered in inguinal hernia trials. The simulations underline the importance of considering clustering as part of the statistical analysis to avoid false-positive and false-negative results, and hence inappropriate study conclusions.
在单个患者中进行多次手术较为常见，会导致数据依赖（聚集）。在研究设计和数据分析中需要考虑这种依赖性。本文进行了一项系统综述，以评估在腹股沟疝试验中是如何处理聚集数据的。通过模拟来估计忽略聚集数据的影响。
系统地检索了 2004 年至 2013 年发表的随机对照试验（RCT），包括接受单侧或双侧腹股沟疝修补术的患者，这些试验来自 PubMed、Embase 和 Cochrane 图书馆。提取了确定处理聚集数据是否适当的研究特征。通过模拟，比较了各种考虑聚集数据的统计方法与通过假设 100 例疝，以不同百分比的患者具有双侧疝来忽略聚类的分析。
在纳入的 50 项包含双侧疝患者的试验中，20 项（40%）未提供有关如何处理聚集数据的信息，18 项（36%）通过按患者而不是按疝评估结局来避免聚类。其余 12 项试验（24%）中没有一项（24%）考虑到设计或分析中的聚类。在模拟中，忽略聚类会导致高达 12%的Ⅰ型错误率增加，根据患者或疝是否为随机化单位，其效能损失高达 15%。
在腹股沟疝试验中很少考虑聚类。模拟强调了在统计分析中考虑聚类的重要性，以避免假阳性和假阴性结果，从而避免得出不适当的研究结论。
