Subgroup identification using covariate-adjusted interaction trees.
We consider the problem of identifying subgroups of participants in a clinical trial that have enhanced treatment effect. Recursive partitioning methods that recursively partition the covariate space based on some measure of between groups treatment effect difference are popular for such subgroup id...
| Publicado en: | Statistics in Medicine Vol. 38; no. 21; pp. 3974 - 3985 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
9/20/2019
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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=137988076&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137988076 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 9/20/2019 vid: 38 iid: 21 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 137988076 137988076 NLM31162690 137988076 10.1002/sim.8214 NLM31162690 137988076 ppf: 3974 ppct: 11 formats: tig: atl: Subgroup identification using covariate-adjusted interaction trees. aug: au: Steingrimsson, Jon Arni Yang, Jiabei affil: Department of Biostatistics, Brown University, Providence Rhode Island sug: subj: Algorithms Clinical Trials Methods Data Analysis, Statistical Prognosis Computer Simulation Human ab: We consider the problem of identifying subgroups of participants in a clinical trial that have enhanced treatment effect. Recursive partitioning methods that recursively partition the covariate space based on some measure of between groups treatment effect difference are popular for such subgroup identification. The most commonly used recursive partitioning method, the classification and regression tree algorithm, first creates a large tree by recursively partitioning the covariate space using some splitting criteria and then selects the final tree from all the subtrees of the large tree. In the context of subgroup identification, calculation of the splitting criteria and the evaluation measure used for final tree selection rely on comparing differences in means between the treatment and control arm. When covariates are prognostic for the outcome, covariate adjusted estimators have the ability to improve efficiency compared to using differences in averages between the treatment and control group. This manuscript develops two covariate adjusted estimators that can be used to both make splitting decisions and for final tree selection. The performance of the resulting covariate adjusted recursive partitioning algorithm is evaluated using simulations and by analyzing a clinical trial that evaluates if motivational interviews improve treatment engagement for substance abusers. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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