Abstract
This paper illustrates how to estimate cumulative and non-cumulative treatment effects in a complex school-based smoking intervention study. The Instrumental Variable method is used to tackle non-compliance and measurement error for a range of treatment exposure measures (binary, ordinal and continuous) in the presence of clustering and dropout. The results are compared to more routine analyses. The empirical findings from this study provide little encouragement for believing that poorly resourced school-based interventions can bring about substantial long-lasting reductions in smoking behaviour but that novel components such as a computer game might have some short-term effect.
Highlights
This paper presents an analysis of the UK component of the European Smoking PreventionFramework Approach (ESFA) study, illustrating and explaining how several difficulties that are commonly encountered in intervention studies may be tackled.Int
School dropout was likely to be related to the importance assigned to the topic, implicit evaluations of treatment effectiveness, staffing and the like and not entirely random
In the last decades there has been a lot of debate on the effectiveness of smoking cessation programs and how this type of interventions should be designed and implemented
Summary
This paper presents an analysis of the UK component of the European Smoking Prevention. The ESFA study was a large cluster-randomised study aiming to reduce smoking among adolescents. In such studies clusters of observations (in our case, pupils in schools) are sampled and randomly assigned to a control or an experimental group and only the subjects belonging to the latter receive the new treatment. Further complexities may arise when, as in the study described in this paper, the treatment is composed of several elements and this set of treatment elements is randomized as a set not to individuals but to, for example, schools.
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