Harmful alcohol use is a major contributor to the global burden of disease  and is considered to be the main cause of nearly 4% of global mortality . The magnitude of this burden partly results from the wide treatment gap, which represents the difference between the prevalence of harmful alcohol use and the number of individuals receiving treatment for harmful alcohol use . The development and use of innovative treatment options (e.g. Internet-based interventions) could narrow the treatment gap for harmful alcohol use.
Internet-based interventions are seen as an attractive option for people who meet harmful alcohol use criteria and who have relatively mild conditions [4–7]. Moreover, these interventions have been found effective in addressing harmful drinking behaviour and improving quality of life (e.g. [7–9]; for a review see: ). There are also indications that Internet-based alcohol interventions are cost-effective .
However, there is notable heterogeneity in treatment outcomes, which several recently published studies have demonstrated. Postel and colleagues  found that three months after baseline, 32% of the alcohol E-therapy participants had not reached a drinking level within the British Medical Association (BMA) drinking guideline limits (no more than 21 standard glasses per week for men, 14 standard glasses per week for women). Riper et al.  conclude that after six months, the majority (83%) of the participants in their ‘Drinking Less’ Internet-based self-help program still consumed more alcohol than the BMA guideline recommends. A study by our research group  found that 71% of the self-help program participants had an unsuccessful treatment outcome six months after baseline.
A number of studies have explored clinical outcome predictors of face-to-face alcohol therapy. These studies have studied the predictive potential of a large number of possible baseline predictors regarding alcohol consumption, other substance use, psychosocial functioning, and demographic characteristics. The research results are mixed: while some authors have identified relevant predictors, other authors have not been able to replicate this. Adamson and colleagues concluded in a recent review  that attempts to synthesize findings on patient predictors of alcohol treatment outcome were also hampered by lack of agreement of the best measure for predictor variables.
For the purpose of the current study, a literature search on PubMed / MEDLINE (1980–2011) using as search term the title words (alcohol OR drink* OR substance *use*) AND (predict* OR outcome* OR treatment) resulted in 5041 articles. The abstracts of potentially relevant articles were screened and those that were considered relevant for our literature overview were retrieved, to identify studies in which the same baseline and/or outcome variables were used as available in our dataset). Based on expert advice, 5 more articles were added to our literature database. A brief overview of the findings reported in 17 publications with the highest relevance to our literature review is presented below.
A number of studies have found a negative relationship between the severity of drinking problems at baseline and clinical outcome [13–15]. McKay & Weiss  however report a positive relationship between baseline drinking problems and clinical outcome. Age of first alcohol consumption, overall duration of alcohol problems and number of previous quit attempts have been linked to treatment outcome . With regard to psychosocial functioning, several measures have been found to predict intervention outcome: self-efficacy [17, 18], motivation to change [18–21], internal locus of control, coping skills, low levels of experienced stress, concern from partners or peers, and a stable social environment [13, 16, 22–24]. Social problems and psychopathology are found to negatively correlate with successful outcome [13, 16, 22, 25]. Particular demographic characteristics, such as age, sex, education level, being of foreign origin, and general socioeconomic status have been linked to clinical outcome [18, 22, 26, 27], although these findings have not always been replicated [16, 22, 28]. To date, only one paper by Riper and colleagues  has assessed which baseline variables predict clinical outcome in Internet-based alcohol interventions. The authors concluded that being female and highly educated were correlated with receiving benefits from an Internet-based self-help intervention.
All in all, it is difficult to define a core set of predictors that should be included in a model aiming to predict treatment outcome. Thus, a large number of possible predictors will be considered for inclusion in the current analysis. Interactions between the possible predictors will also be taken into account, with the aim to test whether a valid predictive model, which can be used as a screening or decision-support tool, can be found. While it is generally assumed that a large sample size will be needed in order to construct and test a model comprising a large number of predictors (and possibly an even larger number of interactions among these predictors), this is not necessarily true . In the current study, a classification tree analysis will be performed using recursive partitioning. Using this data-driven technique, it is feasible to analyze multi-dimensional data in a dataset with a limited sample size . This is an important advantage of recursive partitioning over generalized linear modelling regression analysis. Recursive partitioning can be used to identify variables that are of relevance to future research, but also to create data-driven, evidence-based treatment decision support tools . For example, Swan and colleagues  identified relevant variables when examining the heterogeneity of their outcomes from a smoking cessation intervention using recursive partitioning. Others  have used recursive partitioning in an analysis of pregnant women’s responses to substance use questions, which resulted in a three-item Substance Use Risk Profile-Pregnancy scale. In the current study, recursive partitioning is used in an analysis of data from a randomized controlled trial (RCT) performed in the Netherlands, comparing the effectiveness of Internet-based therapy and Internet-based self-help for harmful alcohol use. Results from this study have been published elsewhere . The current analysis will be performed in order to test whether a screening instrument with acceptable sensitivity and specificity can be developed.