Jonas Benjamin, Tensil Marc-Dennan, Leuschner Fabian, Strüber Evelin, Tossmann Peter
Delphi - Gesellschaft, Berlin, Germany.
Federal Centre for Health Education (BZgA), Cologne, Germany.
Internet Interv. 2019 Jul 26;18:100261. doi: 10.1016/j.invent.2019.100261. eCollection 2019 Dec.
Trials demonstrate the effectiveness of web-based interventions for cannabis-related disorders. For further development of these interventions, it is of vital interest to identify user characteristics which predict treatment response.
Data from a randomized factorial trial on a web-based intervention for cannabis-users (n = 534) was reanalyzed. As potential predictors for later treatment response, 31 variables from the following categories were tested: socio-demographics, substance use and cognitive processing. The association of predictors and treatment outcome was analyzed using unbiased recursive partitioning and represented as classification tree. Predictive performance of the tree was assessed by comparing its cross-validated results to models derived with all-subsets logistic regression and random forest.
Goal commitment (p < .001), the extent of self-reflection (p < .001), the preferred effect of cannabis (p = .005) and initial cannabis use (p = .015) significantly differentiate between successful and non-successful participants in all three analysis methods. The predictive accuracy of all three models is comparable and modest.
Participants who commit to quit using cannabis, who at least have moderate levels of self-reflection and who prefer mild intoxicating effects were most likely to respond to treatment. To predict treatment response on an individual level, the classification tree should only be used as one of several sources of information.: http://www.isrctn.com/ISRCTN99818059.
试验证明了基于网络的干预措施对大麻相关疾病的有效性。为了进一步开发这些干预措施,识别预测治疗反应的用户特征至关重要。
对一项针对大麻使用者的基于网络干预的随机析因试验(n = 534)的数据进行了重新分析。作为后期治疗反应的潜在预测因素,测试了以下类别中的31个变量:社会人口统计学、物质使用和认知加工。使用无偏递归划分分析预测因素与治疗结果之间的关联,并将其表示为分类树。通过将其交叉验证结果与使用全子集逻辑回归和随机森林得出的模型进行比较,评估该树的预测性能。
在所有三种分析方法中,目标承诺(p <.001)、自我反思程度(p <.001)、大麻的偏好效果(p =.005)和初始大麻使用情况(p =.015)在成功和未成功的参与者之间有显著差异。所有三种模型的预测准确性相当且适中。
承诺戒除大麻、至少有中等程度自我反思且偏好轻度中毒效果的参与者最有可能对治疗产生反应。为了在个体层面预测治疗反应,分类树仅应用作多种信息来源之一。: http://www.isrctn.com/ISRCTN99818059