Statistical methodologies to pool across multiple intervention studies
Statistical methodologies to pool across multiple intervention studies
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DOI：
10.1007/s13142-016-0386-8
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发表时间：
2016-06-01
影响因子：
3.6
通讯作者：
Pratt, Charlotte A.
中科院分区：
文献类型：
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作者：
Bangdiwala, Shrikant I.;Bhargava, Alok;Pratt, Charlotte A.
结合和分析复杂的多组分干预研究的异质性随机对照试验数据，或在系统综述中讨论它们并不简单。本文根据NIH主办的研讨会上关于联合体研究中的汇总问题的讨论，描述了在合并研究数据时需要考虑的某些问题（见贝儿等人，《心理衰老》，18（3）：396-405，2003）。几种统计方法进行了说明，并探讨其优点和局限性。无论是对不同的研究数据进行不同的加权，还是通过采用随机效应，都必须认识到不同的汇总方法可能会产生不同的结果。合并可用于对RCT数据进行全面探索性分析，不应被视为取代每项研究的标准分析计划。汇集可能有助于确定可能更有效的干预成分，特别是对于具有某些行为特征的参与者子集。当有统计检验的支持时，合并可以允许对潜在假设进行探索性调查，并用于未来干预措施的设计。
Combining and analyzing data from heterogeneous randomized controlled trials of complex multiple-component intervention studies, or discussing them in a systematic review, is not straightforward. The present article describes certain issues to be considered when combining data across studies, based on discussions in an NIH-sponsored workshop on pooling issues across studies in consortia (see Belle et al. in Psychol Aging, 18(3): 396-405, 2003). Several statistical methodologies are described and their advantages and limitations are explored. Whether weighting the different studies data differently, or via employing random effects, one must recognize that different pooling methodologies may yield different results. Pooling can be used for comprehensive exploratory analyses of data from RCTs and should not be viewed as replacing the standard analysis plan for each study. Pooling may help to identify intervention components that may be more effective especially for subsets of participants with certain behavioral characteristics. Pooling, when supported by statistical tests, can allow exploratory investigation of potential hypotheses and for the design of future interventions.