Heavy tailed distributions of effect sizes in systematic reviews of complex interventions.

<h4>Background</h4>Systematic reviews of complex interventions commonly find heterogeneity of effect sizes among similar interventions which cannot be explained. Commentators have suggested that complex interventions should be viewed as interventions in complex systems. We hypothesised t...

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Main Author: Christopher Burton
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2012-01-01
Series:PLoS ONE
Online Access:https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0034222&type=printable
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author Christopher Burton
author_facet Christopher Burton
author_sort Christopher Burton
collection DOAJ
description <h4>Background</h4>Systematic reviews of complex interventions commonly find heterogeneity of effect sizes among similar interventions which cannot be explained. Commentators have suggested that complex interventions should be viewed as interventions in complex systems. We hypothesised that if this is the case, the distribution of effect sizes from complex interventions should be heavy tailed, as in other complex systems. Thus, apparent heterogeneity may be a feature of the complex systems in which such interventions operate.<h4>Methodology/principal findings</h4>We specified three levels of complexity and identified systematic reviews which reported effect sizes of healthcare interventions at two of these levels (interventions to change professional practice and personal interventions to help smoking cessation). These were compared with each other and with simulated data representing the lowest level of complexity. Effect size data were rescaled across reviews at each level using log-normal parameters and pooled. Distributions were plotted and fitted against the inverse power law (Pareto) and stretched exponential (Weibull) distributions, heavy tailed distributions which are commonly reported in the literature, using maximum likelihood fitting. The dataset included 155 studies of interventions to change practice and 98 studies of helping smoking cessation. Both distributions showed a heavy tailed distribution which fitted best to the inverse power law for practice interventions (exponent = 3.9, loglikelihood = -35.3) and to the stretched exponential for smoking cessation (loglikelihood = -75.2). Bootstrap sensitivity analysis to adjust for possible publication bias against weak results did not diminish the goodness of fit.<h4>Conclusions/significance</h4>The distribution of effect sizes from complex interventions includes heavy tails as typically seen in both theoretical and empirical complex systems. This is in keeping with the idea of complex interventions as interventions in complex systems.
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spelling doaj-art-aded892edda74119816dbd2dca05439f2025-08-20T02:05:35ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0173e3422210.1371/journal.pone.0034222Heavy tailed distributions of effect sizes in systematic reviews of complex interventions.Christopher Burton<h4>Background</h4>Systematic reviews of complex interventions commonly find heterogeneity of effect sizes among similar interventions which cannot be explained. Commentators have suggested that complex interventions should be viewed as interventions in complex systems. We hypothesised that if this is the case, the distribution of effect sizes from complex interventions should be heavy tailed, as in other complex systems. Thus, apparent heterogeneity may be a feature of the complex systems in which such interventions operate.<h4>Methodology/principal findings</h4>We specified three levels of complexity and identified systematic reviews which reported effect sizes of healthcare interventions at two of these levels (interventions to change professional practice and personal interventions to help smoking cessation). These were compared with each other and with simulated data representing the lowest level of complexity. Effect size data were rescaled across reviews at each level using log-normal parameters and pooled. Distributions were plotted and fitted against the inverse power law (Pareto) and stretched exponential (Weibull) distributions, heavy tailed distributions which are commonly reported in the literature, using maximum likelihood fitting. The dataset included 155 studies of interventions to change practice and 98 studies of helping smoking cessation. Both distributions showed a heavy tailed distribution which fitted best to the inverse power law for practice interventions (exponent = 3.9, loglikelihood = -35.3) and to the stretched exponential for smoking cessation (loglikelihood = -75.2). Bootstrap sensitivity analysis to adjust for possible publication bias against weak results did not diminish the goodness of fit.<h4>Conclusions/significance</h4>The distribution of effect sizes from complex interventions includes heavy tails as typically seen in both theoretical and empirical complex systems. This is in keeping with the idea of complex interventions as interventions in complex systems.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0034222&type=printable
spellingShingle Christopher Burton
Heavy tailed distributions of effect sizes in systematic reviews of complex interventions.
PLoS ONE
title Heavy tailed distributions of effect sizes in systematic reviews of complex interventions.
title_full Heavy tailed distributions of effect sizes in systematic reviews of complex interventions.
title_fullStr Heavy tailed distributions of effect sizes in systematic reviews of complex interventions.
title_full_unstemmed Heavy tailed distributions of effect sizes in systematic reviews of complex interventions.
title_short Heavy tailed distributions of effect sizes in systematic reviews of complex interventions.
title_sort heavy tailed distributions of effect sizes in systematic reviews of complex interventions
url https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0034222&type=printable
work_keys_str_mv AT christopherburton heavytaileddistributionsofeffectsizesinsystematicreviewsofcomplexinterventions