A tutorial on individual participant data meta-analysis using Bayesian multilevel modeling to estimate alcohol intervention effects across heterogeneous studies.
This paper provides a tutorial companion for the methodological approach implemented in Huh et al. (2015) that overcame two major challenges for individual participant data (IPD) meta-analysis. Specifically, we show how to validly combine data from heterogeneous studies with varying numbers of treat...
| Publicado en: | Addictive Behaviors Vol. 94; pp. 162 - 171 |
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| Autores principales: | , , , , |
| Formato: | journal article |
| Publicado: |
Elsevier B.V.
Jul2019
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=136675529&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 136675529 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03064603 3A4 jtl: Addictive Behaviors issn: 03064603 maglogo: N pubinfo: dt: Jul2019 vid: 94 pid: 2410 pub: Elsevier B.V. artinfo: ui: 136675529 10.1016/j.addbeh.2019.01.032 ppf: 162 ppct: 9 formats: tig: atl: A tutorial on individual participant data meta-analysis using Bayesian multilevel modeling to estimate alcohol intervention effects across heterogeneous studies. aug: au: Huh, David Mun, Eun-Young Walters, Scott T. Zhou, Zhengyang Atkins, David C. affil: University of Washington, School of Social Work, 4101 15th Ave. NE, Box 354900, Seattle, WA 98195-4900, USA su: Alcohol drinking Multilevel models Meta-analysis Alcohol Computer programming Statistical models Research funding Probability theory Statistics sug: subj: Alcohol drinking Ethyl Alcohol Manufacturing Drinking Places (Alcoholic Beverages) Computer systems design and related services (except video game design and development) Custom Computer Programming Services Other Computer Related Services Multilevel models Meta-analysis Alcohol Computer programming Statistical models Research funding Probability theory Statistics keyword: Bayesian multilevel modeling Brief motivational intervention College drinking Individual participant data Multivariate meta-analysis Bayesian multilevel modeling Brief motivational intervention College drinking Individual participant data Multivariate meta-analysis ab: This paper provides a tutorial companion for the methodological approach implemented in Huh et al. (2015) that overcame two major challenges for individual participant data (IPD) meta-analysis. Specifically, we show how to validly combine data from heterogeneous studies with varying numbers of treatment arms, and how to analyze highly-skewed count outcomes with many zeroes (e.g., alcohol and substance use outcomes) to estimate overall effect sizes. These issues have important implications for the feasibility, applicability, and interpretation of IPD meta-analysis but have received little attention thus far in the applied research literature. We present a Bayesian multilevel modeling approach for combining multi-arm trials (i.e., those with two or more treatment groups) in a distribution-appropriate IPD analysis. Illustrative data come from Project INTEGRATE, an IPD meta-analysis study of brief motivational interventions to reduce excessive alcohol use and related harm among college students. Our approach preserves the original random allocation within studies, combines within-study estimates across all studies, overcomes between-study heterogeneity in trial design (i.e., number of treatment arms) and/or study-level missing data, and derives two related treatment outcomes in a multivariate IPD meta-analysis. This methodological approach is a favorable alternative to collapsing or excluding intervention groups within multi-arm trials, making it possible to directly compare multiple treatment arms in a one-step IPD meta-analysis. To facilitate application of the method, we provide annotated computer code in R along with the example data used in this tutorial. pubtype: Academic Journal doctype: journal article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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