Validating effectiveness of subgroup identification for longitudinal data.
In clinical trials and biomedical studies, treatments are compared to determine which one is effective against illness; however, individuals can react to the same treatment very differently. We propose a complete process for longitudinal data that identifies subgroups of the population that would be...
| Publicado en: | Statistics in Medicine Vol. 37; no. 1; pp. 98 - 107 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/15/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=126842892&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 126842892 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 1/15/2018 vid: 37 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 126842892 126842892 NLM28948635 126842892 10.1002/sim.7500 NLM28948635 126842892 ppf: 98 ppct: 9 formats: tig: atl: Validating effectiveness of subgroup identification for longitudinal data. aug: au: Andrews, Nichole Cho, Hyunkeun affil: Department of Statistics, Western Michigan University, Kalamazoo, MI 49008, USA sug: subj: Models, Statistical Female Human Treatment Outcomes Software Computer Simulation Chaos Theory Algorithms Decision Trees Cognitive Therapy Statistics and Numerical Data Prospective Studies Statistics Depression Therapy Linear Regression Data Analysis, Statistical Female ab: In clinical trials and biomedical studies, treatments are compared to determine which one is effective against illness; however, individuals can react to the same treatment very differently. We propose a complete process for longitudinal data that identifies subgroups of the population that would benefit from a specific treatment. A random effects linear model is used to evaluate individual treatment effects longitudinally where the random effects identify a positive or negative reaction to the treatment over time. With the individual treatment effects and characteristics of the patients, various classification algorithms are applied to build prediction models for subgrouping. While many subgrouping approaches have been developed recently, most of them do not check its validity. In this paper, we further propose a simple validation approach which not only determines if the subgroups used are appropriate and beneficial but also compares methods to predict individual treatment effects. This entire procedure is readily implemented by existing packages in statistical software. The effectiveness of the proposed method is confirmed with simulation studies and analysis of data from the Women Entering Care study on depression. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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