Lisa Dierker, Jennifer Rose, Xianming Tan, Runze Li
{"title":"Uncovering multiple pathways to substance use: a comparison of methods for identifying population subgroups.","authors":"Lisa Dierker, Jennifer Rose, Xianming Tan, Runze Li","doi":"10.1007/s10935-010-0224-6","DOIUrl":null,"url":null,"abstract":"<p><p>This paper describes and compares a selection of available modeling techniques for identifying homogeneous population subgroups in the interest of informing targeted substance use intervention. We present a nontechnical review of the common and unique features of three methods: (a) trajectory analysis, (b) functional hierarchical linear modeling (FHLM), and (c) decision tree methods. Differences among the techniques are described, including required data features, strengths and limitations in terms of the flexibility with which outcomes and predictors can be modeled, and the potential of each technique for helping to inform the selection of targets and timing of substance intervention programs.</p>","PeriodicalId":231565,"journal":{"name":"The Journal of Primary Prevention","volume":" ","pages":"333-48"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3107529/pdf/nihms296394.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Primary Prevention","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s10935-010-0224-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper describes and compares a selection of available modeling techniques for identifying homogeneous population subgroups in the interest of informing targeted substance use intervention. We present a nontechnical review of the common and unique features of three methods: (a) trajectory analysis, (b) functional hierarchical linear modeling (FHLM), and (c) decision tree methods. Differences among the techniques are described, including required data features, strengths and limitations in terms of the flexibility with which outcomes and predictors can be modeled, and the potential of each technique for helping to inform the selection of targets and timing of substance intervention programs.