Bangdiwala Shrikant I, Bhargava Alok, O'Connor Daniel P, Robinson Thomas N, Michie Susan, Murray David M, Stevens June, Belle Steven H, Templin Thomas N, Pratt Charlotte A
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Institute for Social and Health Sciences, University of South Africa, Johannesburg, South Africa.
Transl Behav Med. 2016 Jun;6(2):228-35. doi: 10.1007/s13142-016-0386-8.
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.
合并和分析来自复杂多成分干预研究的异质性随机对照试验的数据，或者在系统评价中对这些数据进行讨论，并非易事。本文基于美国国立卫生研究院（NIH）赞助的关于联盟中跨研究汇总问题的研讨会的讨论内容（见Belle等人发表于《心理学与衰老》，2003年第18卷第3期，第396 - 405页），描述了跨研究合并数据时需要考虑的某些问题。文中介绍了几种统计方法，并探讨了它们的优缺点。无论对不同研究的数据采用不同权重，还是采用随机效应，都必须认识到不同的汇总方法可能会产生不同的结果。汇总可用于对随机对照试验的数据进行全面的探索性分析，不应被视为取代每项研究的标准分析计划。汇总可能有助于识别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。当有统计检验支持时，汇总可以对潜在假设进行探索性研究，并为未来干预措施的设计提供依据。
