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Statistical methodologies to pool across multiple intervention studies

Bangdiwala, SI; Bhargava, A; O’Connor, DP; Robinson, TN; Michie, S; Murray, DM; Stevens, J; ... Pratt, CA; + view all (2016) Statistical methodologies to pool across multiple intervention studies. Translational Behavioral Medicine , 6 (2) pp. 228-235. 10.1007/s13142-016-0386-8. Green open access

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Abstract

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.

Type: Article
Title: Statistical methodologies to pool across multiple intervention studies
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s13142-016-0386-8
Publisher version: http://dx.doi.org/10.1007/s13142-016-0386-8
Language: English
Additional information: Copyright © Society of Behavioral Medicine 2016. The final publication is available at link.springer.com: http://dx.doi.org/10.1007/s13142-016-0386-8.
Keywords: Statistical pooling of studiesm, Random-effects meta-analysis, Study-level meta-regression, Multilevel meta-regression, Multilevel structural models
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Clinical, Edu and Hlth Psychology
URI: https://discovery.ucl.ac.uk/id/eprint/1477659
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