Statistical methodologies to pool across multiple intervention studies Article Swipe
Shrikant I. Bangdiwala
,
Alok Bhargava
,
Daniel P. O’Connor
,
Thomas N. Robinson
,
Susan Michie
,
David M. Murray
,
June Stevens
,
Steven H. Belle
,
Thomas Templin
,
Charlotte Pratt
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1007/s13142-016-0386-8
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s13142-016-0386-8
- https://academic.oup.com/tbm/article-pdf/6/2/228/22066965/13142_2016_article_386.pdf
- OA Status
- bronze
- Cited By
- 91
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2307535985
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2307535985Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s13142-016-0386-8Digital Object Identifier
- Title
-
Statistical methodologies to pool across multiple intervention studiesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-02-03Full publication date if available
- Authors
-
Shrikant I. Bangdiwala, Alok Bhargava, Daniel P. O’Connor, Thomas N. Robinson, Susan Michie, David M. Murray, June Stevens, Steven H. Belle, Thomas Templin, Charlotte PrattList of authors in order
- Landing page
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https://doi.org/10.1007/s13142-016-0386-8Publisher landing page
- PDF URL
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https://academic.oup.com/tbm/article-pdf/6/2/228/22066965/13142_2016_article_386.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/tbm/article-pdf/6/2/228/22066965/13142_2016_article_386.pdfDirect OA link when available
- Concepts
-
Pooling, Psychological intervention, Weighting, Exploratory data analysis, Computer science, Data science, Intervention (counseling), Psychology, Data mining, Machine learning, Management science, Artificial intelligence, Applied psychology, Medicine, Engineering, Psychiatry, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
91Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 14, 2023: 13, 2022: 10, 2021: 16Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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