Evidence map›Paper›PMID 27356993›Full record

ArticleTranslational behavioral medicine2016

Statistical methodologies to pool across multiple intervention studies.

Shrikant I Bangdiwala, Alok Bhargava, Daniel P O'Connor, Thomas N Robinson, Susan Michie, David M Murray, June Stevens, Steven H Belle, Thomas N Templin, Charlotte A Pratt

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational behavioral medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 8 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

52 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Shrikant I BangdiwalaDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. kant@unc.edu.
Alok BhargavaUniversity of Maryland School of Public Policy, College Park, MD, USA.
Daniel P O'ConnorDepartment of Health and Human Performance, Texas Obesity Research Center, University of Houston, Houston, TX, USA.
Thomas N RobinsonCenter for Healthy Weight, Stanford University, Stanford, CA, USA.
Susan MichieCentre for Behaviour Change and Department of Clinical, Educational and Health Psychology, University College London, London, UK.
David M MurrayBiostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Rockville, MD, USA.
June StevensDepartment of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Steven H BelleDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA.
Thomas N TemplinOffice of Health Research, College of Nursing, Wayne State University, Detroit, MI, USA.
Charlotte A PrattDivision of Cardiovascular Sciences, National Heart Lung and Blood Institute, Bethesda, MD, USA.

Funding

Clinic, Family & Community Collaboration to Treat Overweight and Obese ChildrenU01HL103629 · NHLBI · STANFORD UNIVERSITY · PI ROBINSON, THOMAS N. · 2010 to 2016
$12.6M
Medical Research Council G0901474NHLBI NIH HHS U01 HL103629
6 · The paper itself

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.

Indexed as

Data Interpretation, StatisticalFemaleHumansMaleMeta-Analysis as TopicModels, StatisticalResearch DesignMultilevel meta-regressionMultilevel structural modelsRandom-effects meta-analysisStatistical pooling of studiesStudy-level meta-regression

Identifiers

PMID27356993
PMCPMC4927450

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.