Dataset Information

A framework to evaluate whether to pool or separate behaviors in a multilayer network.

ABSTRACT: A multilayer network approach combines different network layers, which are connected by interlayer edges, to create a single mathematical object. These networks can contain a variety of information types and represent different aspects of a system. However, the process for selecting which information to include is not always straightforward. Using data on 2 agonistic behaviors in a captive population of monk parakeets (Myiopsitta monachus), we developed a framework for investigating how pooling or splitting behaviors at the scale of dyadic relationships (between 2 individuals) affects individual- and group-level social properties. We designed 2 reference models to test whether randomizing the number of interactions across behavior types results in similar structural patterns as the observed data. Although the behaviors were correlated, the first reference model suggests that the 2 behaviors convey different information about some social properties and should therefore not be pooled. However, once we controlled for data sparsity, we found that the observed measures corresponded with those from the second reference model. Hence, our initial result may have been due to the unequal frequencies of each behavior. Overall, our findings support pooling the 2 behaviors. Awareness of how selected measurements can be affected by data properties is warranted, but nonetheless our framework disentangles these efforts and as a result can be used for myriad types of behaviors and questions. This framework will help researchers make informed and data-driven decisions about which behaviors to pool or separate, prior to using the data in subsequent multilayer network analyses.

SUBMITTER: van der Marel A

PROVIDER: S-EPMC7901760 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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A framework to evaluate whether to pool or separate behaviors in a multilayer network.

van der Marel Annemarie A Prasher Sanjay S Carminito Chelsea C O'Connell Claire L CL Phillips Alexa A Kluever Bryan M BM Hobson Elizabeth A EA

Current zoology 20201226 1

A multilayer network approach combines different network layers, which are connected by interlayer edges, to create a single mathematical object. These networks can contain a variety of information types and represent different aspects of a system. However, the process for selecting which information to include is not always straightforward. Using data on 2 agonistic behaviors in a captive population of monk parakeets (<i>Myiopsitta monachus</i>), we developed a framework for investigating how p ...[more]

PMID: 33654494

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Project description:BackgroundThe unrelated mean effects (UME) model has been proposed for evaluating the consistency assumption globally in the network of interventions. However, the UME model does not accommodate multiarm trials properly and omits comparisons between nonbaseline interventions in the multiarm trials not investigated in 2-arm trials.MethodsWe proposed a refinement of the UME model that tackles the limitations mentioned above. We also accompanied the scatterplots on the posterior mean deviance contributions of the trial arms under the network meta-analysis (NMA) and UME models with Bland-Altman plots to detect outlying trials contributing to poor model fit. We applied the refined and original UME models to 2 networks with multiarm trials.ResultsThe original UME model omitted more than 20% of the observed comparisons in both networks. The thorough inspection of the individual data points' deviance contribution using complementary plots in conjunction with the measures of model fit and the estimated between-trial variance indicated that the refined and original UME models revealed possible inconsistency in both examples.ConclusionsThe refined UME model allows proper accommodation of the multiarm trials and visualization of all observed evidence in complex networks of interventions. Furthermore, considering several complementary plots to investigate deviance helps draw informed conclusions on the possibility of global inconsistency in the network.HighlightsWe have refined the unrelated mean effects (UME) model to incorporate multiarm trials properly and to estimate all observed comparisons in complex networks of interventions.Forest plots with posterior summaries of all observed comparisons under the network meta-analysis and refined UME model can uncover the consequences of potential inconsistency in the network.Using complementary plots to investigate the individual data points' deviance contribution in conjunction with model fit measures and estimated heterogeneity aid in detecting possible inconsistency.

Dataset Information

A framework to evaluate whether to pool or separate behaviors in a multilayer network.

Publications

A framework to evaluate whether to pool or separate behaviors in a multilayer network.

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