Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data.
Objective: Decision-tree methods are machine-learning methods which provide results that are relatively easy to interpret and apply by human decision makers. The resulting decision trees show how baseline patient characteristics can be combined to predict treatment outcomes for individual patients,...
| Publicado en: | Psychotherapy Research Vol. 31; no. 3; pp. 313 - 326 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Mar2021
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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=148720781&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 148720781 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10503307 10T jtl: Psychotherapy Research issn: 10503307 maglogo: N pubinfo: dt: Mar2021 vid: 31 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 148720781 10.1080/10503307.2020.1785037 ppf: 313 ppct: 13 formats: tig: atl: Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data. aug: au: Fokkema, Marjolein Edbrooke-Childs, Julian Wolpert, Miranda affil: Department of Methods & Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands Evidence Based Practice Unit, Anna Freud Centre/UCL, London, UK su: United Kingdom Longitudinal method Random forest algorithms Decision trees Trees Treatment effectiveness sug: subj: United Kingdom Flower, Nursery Stock, and Florists' Supplies Merchant Wholesalers Longitudinal method Random forest algorithms Decision trees Trees Treatment effectiveness keyword: decision making decision-tree methods mixed-effects models multilevel data subgroup detection decision making decision-tree methods mixed-effects models multilevel data subgroup detection ab: Objective: Decision-tree methods are machine-learning methods which provide results that are relatively easy to interpret and apply by human decision makers. The resulting decision trees show how baseline patient characteristics can be combined to predict treatment outcomes for individual patients, for example. This paper introduces GLMM trees, a decision-tree method for multilevel and longitudinal data. Method: To illustrate, we apply GLMM trees to a dataset of 3,256 young people (mean age 11.33, 48% girls) receiving treatment at one of several mental-health service providers in the UK. Two treatment outcomes (mental-health difficulties scores corrected for baseline) were regressed on 18 demographic, case and severity characteristics at baseline. We compared the performance of GLMM trees with that of traditional GLMMs and random forests. Results: GLMM trees yielded modest predictive accuracy, with cross-validated multiple R values of.18 and.25. Predictive accuracy did not differ significantly from that of traditional GLMMs and random forests, while GLMM trees required evaluation of a lower number of variables. Conclusion: GLMM trees provide a useful data-analytic tool for clinical prediction problems. The supplemental material provides a tutorial for replicating the GLMM tree analyses in R. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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