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Background and aimsBehavioural smoking cessation trials have employed comparators that vary considerably between trials. Although some previous meta-analyses made attempts to account for variability in comparators, these relied on subsets of trials and incomplete data on comparators. This study aimed to estimate the relative effectiveness of (individual) smoking cessation interventions, while accounting for variability in comparators using comprehensive data on experimental and comparator interventions.MethodsSystematic review and meta-regression including 172 randomised controlled trials with at least 6 months' follow-up and biochemically verified smoking cessation. Authors were contacted to obtain unpublished information. This information was coded in terms of active content and attributes of the study population and methods. Meta-regression was used to create a model predicting smoking cessation outcomes. This model was used to re-estimate intervention effects, as if all interventions have been evaluated against the same comparators. Outcome measures included log odds of smoking cessation for the meta-regression models and smoking cessation differences and ratios to compare relative effectiveness.ResultsThe meta-regression model predicted smoking cessation rates well (pseudo R2 =.44). Standardizing the comparator had substantial impact on conclusions regarding the (relative) effectiveness of trials and types of intervention. Compared with a 'no support comparator', self-help was 1.33 times (95% confidence interval [CI]=1.16-1.49), brief physician advice 1.61 times (95%CI=1.31-1.90) nurse individual counselling 1.76 times (95%CI=1.62-1.90), psychologist individual counselling 2.04 times (95%CI=1.95-2.15), and individual group psychologist interventions 2.06 times (95%CI=1.92-2.20) more effective. Notably, more elaborate experimental interventions (e.g., psychologist counselling) were typically compared with more elaborate comparators, masking their effectiveness.ConclusionsComparator variability and underreporting of comparators obscures the interpretation, comparison and generalisability of behavioural smoking cessation trials. Comparator variability should thus be taken into account when interpreting and synthesising evidence from trials. Otherwise, policy makers, practitioners and researchers may draw incorrect conclusions about the (cost)effectiveness of smoking cessation interventions and their constituent components.

Original publication




Journal article


Addiction (Abingdon, England)

Publication Date



Radboud Institute for Health Sciences, Radboud University Medical Centre, Nijmegen, The Netherlands.