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Background and aims A chronic care strategy could potentially enhance the reach and effectiveness of smoking treatment by providing effective interventions for all smokers, including those who are initially unwilling to quit. This paper describes the conceptual bases of a National Cancer Institute-funded research program designed to develop an optimized, comprehensive, chronic care smoking treatment. Methods This research is grounded in three methodological approaches: (1) the Phase-Based Model, which guides the selection of intervention components to be experimentally evaluated for the different phases of smoking treatment (motivation, preparation, cessation, and maintenance); (2) the Multiphase Optimization Strategy (MOST), which guides the screening of intervention components via efficient experimental designs and, ultimately, the assembly of promising components into an optimized treatment package; and (3) pragmatic research methods, such as electronic health record recruitment, that facilitate the efficient translation of research findings into clinical practice. Using this foundation and working in primary care clinics, we conducted three factorial experiments (reported in three accompanying papers) to screen 15 motivation, preparation, cessation and maintenance phase intervention components for possible inclusion in a chronic care smoking treatment program. Results This research identified intervention components with relatively strong evidence of effectiveness at particular phases of smoking treatment and it demonstrated the efficiency of the MOST approach in terms both of the number of intervention components tested and of the richness of the information yielded. Conclusions A new, synthesized research approach efficiently evaluates multiple intervention components to identify promising components for every phase of smoking treatment. Many intervention components interact with one another, supporting the use of factorial experiments in smoking treatment development. [ABSTRACT FROM AUTHOR]

Background and Aims It has been proposed that more use should be made of Bayes factors in hypothesis testing in addiction research. Bayes factors are the ratios of the likelihood of a specified hypothesis (e.g. an intervention effect within a given range) to another hypothesis (e.g. no effect). They are particularly important for differentiating lack of strong evidence for an effect and evidence for lack of an effect. This paper reviewed randomized trials reported in Addiction between January and June 2013 to assess how far Bayes factors might improve the interpretation of the data. Methods Seventy-five effect sizes and their standard errors were extracted from 12 trials. Seventy-three per cent ( n = 55) of these were non-significant (i.e. P > 0.05). For each non-significant finding a Bayes factor was calculated using a population effect derived from previous research. In sensitivity analyses, a further two Bayes factors were calculated assuming clinically meaningful and plausible ranges around this population effect. Results Twenty per cent ( n = 11) of the non-significant Bayes factors were < ⅓ and 3.6% ( n = 2) were > 3. The other 76.4% ( n = 42) of Bayes factors were between ⅓ and 3. Of these, 26 were in the direction of there being an effect (Bayes factor > 1 and < 3); 12 tended to favour the hypothesis of no effect (Bayes factor < 1 and > ⅓); and for four there was no evidence either way (Bayes factor = 1). In sensitivity analyses, 13.3% of Bayes Factors were < ⅓ ( n = 20), 62.7% ( n = 94) were between ⅓ and 3 and 24.0% ( n = 36) were > 3, showing good concordance with the main results. Conclusions Use of Bayes factors when analysing data from randomized trials of interventions in addiction research can provide important information that would lead to more precise conclusions than are obtained typically using currently prevailing methods. [ABSTRACT FROM AUTHOR]

Aim To review randomized controlled trials to assess efficacy of a prize-based contingency management procedure in reducing substance use (where a drug-free breath or urine sample provides a chance of winning a prize). Methods A meta-analysis was conducted on papers published from January 2000 to February 2013 to determine the effect size of studies comparing prize-based contingency management to a treatment-as-usual control condition (k = 19 studies). Parallel analyses evaluated the efficacy of both short- (k = nine studies) and long-term outcomes (k = six studies) of prize-based contingency management. Results The average end-of-treatment effect size (Cohen’s d) was 0.46 [95% confidence interval (CI) = 0.37, 0.54]. This effect size decreased at the short-term (≤3-month) post-intervention follow-up to 0.33 (95% CI = 0.12, 0.54) and at the 6-month follow-up time-point there was no detectable effect [d = −0.09 (95% CI = −0.28, 0.10)]. Conclusion Adding prize-based contingency management to behavioral support for substance use disorders can increase short-term abstinence, but the effect does not appear to persist to 6 months. [ABSTRACT FROM AUTHOR]