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分层整群随机试验的分析与报告——一项系统调查

Analysis and reporting of stratified cluster randomized trials-a systematic survey.

作者信息

Borhan Sayem, Papaioannou Alexandra, Ma Jinhui, Adachi Jonathan, Thabane Lehana

机构信息

Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada.

Biostatistics Unit, Research Institute of St Joseph's Healthcare, Hamilton, ON, Canada.

出版信息

Trials. 2020 Nov 17;21(1):930. doi: 10.1186/s13063-020-04850-w.

Abstract

BACKGROUND

In order to correctly assess the effect of intervention from stratified cluster randomized trials (CRTs), it is necessary to adjust for both clustering and stratification, as failure to do so leads to misleading conclusions about the intervention effect. We have conducted a systematic survey to examine the current practices about analysis and reporting of stratified CRTs.

METHOD

We used the search terms to identify the stratified CRTs from MEDLINE since the inception to July 2019. In phase 1, we screened the title and abstract for English-only studies and selected, including the main results paper of the identified protocols, for the next phase. In phase 2, we screened the full text and selected studies for data abstraction. The data abstraction form was piloted and developed using the REDCap. We abstracted data on multiple design and methodological aspects of the study including whether the primary method adjusted for both clustering and stratification, reporting of sample size, randomization, and results.

RESULTS

We screened 2686 studies in the phase 1 and selected 286 studies for phase 2-among them 185 studies were selected for data abstraction. Most of the selected studies were two-arm 140/185 (76%) and parallel-group 165/185 (89%) trials. Among these 185 studies, 27 (15%) of them did not provide any sample size or power calculation, while 105 (57%) studies did not mention any method used for randomization within each stratum. Further, 43 (23%) and 150 (81%) of 185 studies did not provide the definition of all the strata, while more than 60% of the studies did not include all the stratification variable(s) in the flow chart or baseline characteristics table. More than half 114/185 (62%) of the studies did not adjust the primary method for both clustering and stratification.

CONCLUSION

Stratification helps to achieve the balance among intervention groups. However, to correctly assess the intervention effect from stratified CRTs, it is important to adjust the primary analysis for both stratification and clustering. There are significant deficiencies in the reporting of methodological aspects of stratified CRTs, which require substantial improvements in several areas including definition of strata, inclusion of stratification variable(s) in the flow chart or baseline characteristics table, and reporting the stratum-specific number of clusters and individuals in the intervention groups.

摘要

背景

为了正确评估分层整群随机试验(CRT)的干预效果,有必要对整群效应和分层效应进行调整,否则会得出关于干预效果的误导性结论。我们进行了一项系统调查,以研究分层CRT分析和报告的当前做法。

方法

我们使用检索词从MEDLINE数据库中识别自数据库建立至2019年7月期间的分层CRT。在第一阶段,我们筛选了仅英文研究的标题和摘要,并选择了包括已识别方案的主要结果论文进入下一阶段。在第二阶段,我们筛选全文并选择研究进行数据提取。数据提取表通过REDCap进行试用和开发。我们提取了研究多个设计和方法学方面的数据,包括主要方法是否对整群效应和分层效应都进行了调整、样本量报告、随机化和结果。

结果

我们在第一阶段筛选了2686项研究,并选择了286项研究进入第二阶段,其中185项研究被选作数据提取。大多数入选研究为双臂试验140/185(76%)和平行组试验165/185(89%)。在这185项研究中,27项(15%)未提供任何样本量或效能计算,而105项(57%)研究未提及各层内随机化所使用的任何方法。此外,185项研究中有43项(23%)和150项(81%)未提供所有层的定义,而超过60%的研究未在流程图或基线特征表中纳入所有分层变量。超过一半114/185(62%)的研究未对主要方法的整群效应和分层效应进行调整。

结论

分层有助于在干预组之间实现平衡。然而,为了正确评估分层CRT的干预效果,对分层和整群效应进行主要分析的调整很重要。分层CRT方法学方面的报告存在重大缺陷,需要在几个方面进行实质性改进,包括层的定义、在流程图或基线特征表中纳入分层变量,以及报告干预组中各层的整群数和个体数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c91e/7672868/7d605b0cbaeb/13063_2020_4850_Fig1_HTML.jpg

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