Seeing the trees despite the forest: Applying recursive partitioning to the evaluation of drug treatment retention
Seeing the trees despite the forest: Applying recursive partitioning to the evaluation of drug treatment retention
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DOI：
10.1016/j.jsat.2008.03.005
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发表时间：
2009-01-01
影响因子：
3.9
通讯作者：
Longshore, Douglas
中科院分区：
文献类型：
--
作者：
Hellemann, Gerhard;Conner, Bradley T.;Longshore, Douglas
目的：本研究的目的是证明递归划分（RP）在药物治疗研究中分析过程和结果数据的效用。介绍了RP的基本方法，并将其应用于处理滞留量的预测。方法：共有315人随机分配到两种治疗条件之一；289例（91.7%）完成了综合基线评估。在52周的随访中评估治疗效果。研究结果：RP方法成功地从195个输入变量中生成了一个预测药物治疗保留的简约决策树。药物使用的严重程度（以时间快速球的长度表示）、犯罪行为（以财产犯罪的历史表示）、洞察力水平、社会网络和服用时的年龄是治疗保留的预测因素。据估计，该模型可以解释人口中32%的变异。结论：RP支持这样一种观点，即存在治疗保留的早期指标，并且根据个人需求量身定制的特定方法在治疗参与和保留方面可能比典型的“一刀切”方法更成功。结果还证明了RP用于检测不同和相互依赖的预测因子之间的复杂关系的实用性。(C) 2009爱思唯尔公司版权所有。
Aims: The aim of this study is to demonstrate the utility of recursive partitioning (RP) for analyzing process and outcome data in drug treatment research. The basic methodology of RP is introduced and applied to the prediction of treatment retention. Methods: A total of 315 individuals randomly assigned to one of two treatment conditions; 289 (91.7%) completed a comprehensive baseline assessment battery. Treatment retention was assessed at a 52-week follow-up interview. Findings: The RP approach was successful in generating a parsimonious decision tree that predicted drug treatment retention from the 195 input variables. Severity of drug use (as indicated by length of time speedballing), criminal behavior (as indicated by history of property crimes), level of insight, social network, and age at intake were predictive of treatment retention. The model is estimated to explain 32% of the variability in the population. Conclusions: RP supports the notion that there are early indicators of treatment retention and that specific approaches that are tailored to individuals' needs will be potentially more successful in treatment engagement and retention than the typical "one size fits all" approach. The results also demonstrate the utility of RP for the detection of complex relationships between diverse and interdependent predictors. (C) 2009 Elsevier Inc. All rights reserved.