Baseline predictors of treatment outcome in Internet-based alcohol interventions: a recursive partitioning analysis alongside a randomized trial

BMC Public Health

Gerard M Schippers¹  Maarten WJ Koeter¹  Matthijs Blankers¹ 

[1] Academic Medical Centre, Department of Psychiatry, Amsterdam Institute for Addiction Research, University of Amsterdam, Amsterdam, The Netherlands

关键词: Recursive partitioning;    RCT;    Outcome predictors;    Intervention;    Internet;    Alcohol;   

Others  :  1162226
DOI  :  10.1186/1471-2458-13-455

 received in 2012-09-17, accepted in 2013-03-12,  发布年份 2013

【 摘 要 】

Background

Internet-based interventions are seen as attractive for harmful users of alcohol and lead to desirable clinical outcomes. Some participants will however not achieve the desired results. In this study, harmful users of alcohol have been partitioned in subgroups with low, intermediate or high probability of positive treatment outcome, using recursive partitioning classification tree analysis.

Methods

Data were obtained from a randomized controlled trial assessing the effectiveness of two Internet-based alcohol interventions. The main outcome variable was treatment response, a dichotomous outcome measure for treatment success. Candidate predictors for the classification analysis were first selected using univariate regression. Next, a tree decision model to classify participants in categories with a low, medium and high probability of treatment response was constructed using recursive partitioning software.

Results

Based on literature review, 46 potentially relevant baseline predictors were identified. Five variables were selected using univariate regression as candidate predictors for the classification analysis. Two variables were found most relevant for classification and selected for the decision tree model: ‘living alone’, and ‘interpersonal sensitivity’. Using sensitivity analysis, the robustness of the decision tree model was supported.

Conclusions

Harmful alcohol users in a shared living situation, with high interpersonal sensitivity, have a significantly higher probability of positive treatment outcome. The resulting decision tree model may be used as part of a decision support system but is on its own insufficient as a screening algorithm with satisfactory clinical utility.

Trial registration

Netherlands Trial Register (Cochrane Collaboration): NTR-TC1155.

【 授权许可】

   
2013 Blankers et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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