title: Statistical methodologies to pool across multiple intervention studies.
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.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. kant@unc.edu.|Institute for Social and Health Sciences, University of South Africa, Johannesburg, South Africa. kant@unc.edu.
authors.forename: Shrikant I
authors.initials: SI
authors.lastname: Bangdiwala
authors.affiliation: University of Maryland School of Public Policy, College Park, MD, USA.
authors.forename: Alok
authors.initials: A
authors.lastname: Bhargava
authors.affiliation: Department of Health and Human Performance, Texas Obesity Research Center, University of Houston, Houston, TX, USA.
authors.forename: Daniel P
authors.initials: DP
authors.lastname: O'Connor
authors.affiliation: Center for Healthy Weight, Stanford University, Stanford, CA, USA.
authors.forename: Thomas N
authors.initials: TN
authors.lastname: Robinson
authors.affiliation: Centre for Behaviour Change and Department of Clinical, Educational and Health Psychology, University College London, London, UK.
authors.forename: Susan
authors.initials: S
authors.lastname: Michie
authors.affiliation: Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Rockville, MD, USA.
authors.forename: David M
authors.initials: DM
authors.lastname: Murray
authors.affiliation: Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: June
authors.initials: J
authors.lastname: Stevens
authors.affiliation: Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Steven H
authors.initials: SH
authors.lastname: Belle
authors.affiliation: Office of Health Research, College of Nursing, Wayne State University, Detroit, MI, USA.
authors.forename: Thomas N
authors.initials: TN
authors.lastname: Templin
authors.affiliation: Division of Cardiovascular Sciences, National Heart Lung and Blood Institute, Bethesda, MD, USA.
authors.forename: Charlotte A
authors.initials: CA
authors.lastname: Pratt
mesh_terms: D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D012107:Research Design
keywords: Multilevel meta-regression; Multilevel structural models; Random-effects meta-analysis; Statistical pooling of studies; Study-level meta-regression