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Introduction: Depression/anxiety disorders and smoking behavior often begin in adolescence as cooccurring phenomena. Epidemiologically, the relationship between them is bidirectional, Objective and Aims: To examine both adolescent smoking as a predictor of depression/anxiety disorders, and the effect of these disorders on adolescent smoking. Method: A representative sample of Catalan adolescents (N = 3008) participating in (PFI) a longitudinal study were interviewed about smoking behavior at baseline (T1) and at two follow-up assessments (T2 and T3). In interviews at baseline (T1) and at T4, the parents were asked whether their daughters/sons had a diagnosis of depression or anxiety. Smoking behavior was defined as smoking at least once a week. Age, sex and household income were included. Results: Depression/anxiety at T1 increases the risk of smoking behavior at T3 [odds ratio (OR), 1.97, 95% confidence interval (CI) 1.24-3.14] compared with undiagnosed adolescents. After adjusting for age and sex, the risk remains, but after adjusting for sex, age and income, the risk of smoking behavior decreases as household income increases [OR 1.6, 95% CI 0.9-2.85]. Alternatively, smoking behavior at T3 increases the risk of depression/anxiety at T4 [OR 1.7, 95% CI 1.1-2.5] compared with non-smokers. After adjusting for age and sex, the risk remains, but after adjusting for sex, age and income, the risk of depression/anxiety decreases as household income increases [OR 1.48, 95% CI 0.93-2.36]. Conclusions: Our findings provide the first evidence of a two-way relationship between adolescent smoking and depression/anxiety disorders in a community sample in Spain.

The designer of a clinical trial needs to make many assumptions about real-life practice based on prior knowledge. Simulation allows us to learn from experience by using the information obtained from a trial to improve the original estimators of population parameters. We propose using data from a previous trial to formulate assumptions that can be used to simulate trials and thus improve the design of new trials. To demonstrate our method, we used data from a real clinical trial which had been designed to evaluate cholesterol level changes as a surrogate marker for lipodystrophy in HIV patients. We were able to identify the optimal design that would have minimised the cost of a trial subject to a statistical power constraint which could then be used to design a new trial. In particular, we focused on three factors: the distribution of cholesterol levels in HIV patients, trial recruitment rates and trial dropout rates. We were able to verify our hypothesis that the total cost resulting from carrying out a clinical trial can be minimised by applying simulation models as an alternative to conventional approaches. In our findings the simulation model proved to be very intuitive and a useful method for testing the performance of investigators' assumptions and generating an optimal clinical trial design before being put into practice in the real world. In addition, we concluded that simulation models provide a more accurate determination of power than conventional approaches, thus minimising the total cost of clinical trials.

Combined therapy (E/N) is, on the basis of the assumptions made in the model, an efficient therapy option. Therefore, it can be recommended for prescription.

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