Objectives To estimate how far changes in the prevalence of electronic cigarette (e-cigarette) use in England have been associated with changes in quit success, quit attempts, and use of licensed medication and behavioural support in quit attempts. Design Time series analysis of population trends. Participants Participants came from the Smoking Toolkit Study, which involves repeated, cross sectional household surveys of individuals aged 16 years and older in England. Data were aggregated on about 1200 smokers quarterly between 2006 and 2015. Monitoring data were also used from the national behavioural support programme; during the study, 8 029 012 quit dates were set with this programme. Setting England. Main outcome measures Prevalence of e-cigarette use in current smokers and during a quit attempt were used to predict quit success. Prevalence of e-cigarette use in current smokers was used to predict rate of quit attempts. Percentage of quit attempts involving e-cigarette use was also used to predict quit attempts involving use of prescription treatments, nicotine replacement therapy (NRT) on prescription and bought over the counter, and use of behavioural support. Analyses involved adjustment for a range of potential confounders. Results The success rate of quit attempts increased by 0.098% (95% confidence interval 0.064 to 0.132; P<0.001) and 0.058% (0.038 to 0.078; P<0.001) for every 1% increase in the prevalence of e-cigarette use by smokers and e-cigarette use during a recent quit attempt, respectively. There was no clear evidence for an association between e-cigarette use and rate of quit attempts (β 0.025; 95% confidence interval −0.035 to 0.085; P=0.41), use of NRT bought over the counter (β 0.006; −0.088 to 0.077; P=0.89), use of prescription treatment (β −0.070; −0.152 to 0.013; P=0.10), or use of behavioural support (β −0.013; −0.102 to 0.077; P=0.78). A negative association was found between e-cigarette use during a recent quit attempt and use of NRT obtained on prescription (β −0.098; −0.189 to −0.007; P=0.04). Conclusion Changes in prevalence of e-cigarette use in England have been positively associated with the success rates of quit attempts. No clear association has been found between e-cigarette use and the rate of quit attempts or the use of other quitting aids, except for NRT obtained on prescription, where the association has been negative. Study registration The analysis plan was preregistered (https://osf.io/fbgj2/).

Stepped wedge trials (SWTs) can be considered as a variant of a clustered randomised trial, although in many ways they embed additional complications from the point of view of statistical design and analysis. While the literature is rich for standard parallel or clustered randomised clinical trials (CRTs), it is much less so for SWTs. The specific features of SWTs need to be addressed properly in the sample size calculations to ensure valid estimates of the intervention effect. We critically review the available literature on analytical methods to perform sample size and power calculations in a SWT. In particular, we highlight the specific assumptions underlying currently used methods and comment on their validity and potential for extensions. Finally, we propose the use of simulation-based methods to overcome some of the limitations of analytical formulae. We performed a simulation exercise in which we compared simulation-based sample size computations with analytical methods and assessed the impact of varying the basic parameters to the resulting sample size/power, in the case of continuous and binary outcomes and assuming both cross-sectional data and the closed cohort design. We compared the sample size requirements for a SWT in comparison to CRTs based on comparable number of measurements in each cluster. In line with the existing literature, we found that when the level of correlation within the clusters is relatively high (for example, greater than 0.1), the SWT requires a smaller number of clusters. For low values of the intracluster correlation, the two designs produce more similar requirements in terms of total number of clusters. We validated our simulation-based approach and compared the results of sample size calculations to analytical methods; the simulation-based procedures perform well, producing results that are extremely similar to the analytical methods. We found that usually the SWT is relatively insensitive to variations in the intracluster correlation, and that failure to account for a potential time effect will artificially and grossly overestimate the power of a study. We provide a framework for handling the sample size and power calculations of a SWT and suggest that simulation-based procedures may be more effective, especially in dealing with the specific features of the study at hand. In selected situations and depending on the level of intracluster correlation and the cluster size, SWTs may be more efficient than comparable CRTs. However, the decision about the design to be implemented will be based on a wide range of considerations, including the cost associated with the number of clusters, number of measurements and the trial duration.

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.

In a stepped wedge, cluster randomised trial, clusters receive the intervention at different time points, and the order in which they received it is randomised. Previous systematic reviews of stepped wedge trials have documented a steady rise in their use between 1987 and 2010, which was attributed to the design’s perceived logistical and analytical advantages. However, the interventions included in these systematic reviews were often poorly reported and did not adequately describe the analysis and/or methodology used. Since 2010, a number of additional stepped wedge trials have been published. This article aims to update previous systematic reviews, and consider what interventions were tested and the rationale given for using a stepped wedge design. We searched PubMed, PsychINFO, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Web of Science, the Cochrane Library and the Current Controlled Trials Register for articles published between January 2010 and May 2014. We considered stepped wedge randomised controlled trials in all fields of research. We independently extracted data from retrieved articles and reviewed them. Interventions were then coded using the functions specified by the Behaviour Change Wheel, and for behaviour change techniques using a validated taxonomy. Our review identified 37 stepped wedge trials, reported in 10 articles presenting trial results, one conference abstract, 21 protocol or study design articles and five trial registrations. These were mostly conducted in developed countries (n = 30), and within healthcare organisations (n = 28). A total of 33 of the interventions were educationally based, with the most commonly used behaviour change techniques being ‘instruction on how to perform a behaviour’ (n = 32) and ‘persuasive source’ (n = 25). Authors gave a wide range of reasons for the use of the stepped wedge trial design, including ethical considerations, logistical, financial and methodological. The adequacy of reporting varied across studies: many did not provide sufficient detail regarding the methodology or calculation of the required sample size. The popularity of stepped wedge trials has increased since 2010, predominantly in high-income countries. However, there is a need for further guidance on their reporting and analysis.

There is consistent evidence that individuals in higher socioeconomic status groups are more likely to report exceeding recommended drinking limits, but those in lower socioeconomic status groups experience more alcohol-related harm. This has been called the ‘alcohol harm paradox’. Such studies typically use standard cut-offs to define heavy drinking, which are exceeded by a large proportion of adults. Our study pools data from six years (2008–2013) of the population-based Health Survey for England to test whether the socioeconomic distribution of more extreme levels of drinking could help explain the paradox. The study included 51,498 adults from a representative sample of the adult population of England for a cross-sectional analysis of associations between socioeconomic status and self-reported drinking. Heavy weekly drinking was measured at four thresholds, ranging from 112 g+/168 g + (alcohol for women/men, or 14/21 UK standard units) to 680 g+/880 g + (or 85/110 UK standard units) per week. Heavy episodic drinking was also measured at four thresholds, from 48 g+/64 g + (or 6/8 UK standard units) to 192 g+/256 g + (or 24/32 UK standard units) in one day. Socioeconomic status indicators were equivalised household income, education, occupation and neighbourhood deprivation. Lower socioeconomic status was associated with lower likelihoods of exceeding recommended limits for weekly and episodic drinking, and higher likelihoods of exceeding more extreme thresholds. For example, participants in routine or manual occupations had 0.65 (95 % CI 0.57–0.74) times the odds of exceeding the recommended weekly limit compared to those in ‘higher managerial’ occupations, and 2.15 (95 % CI 1.06–4.36) times the odds of exceeding the highest threshold. Similarly, participants in the lowest income quintile had 0.60 (95 % CI 0.52–0.69) times the odds of exceeding the recommended weekly limit when compared to the highest quintile, and 2.30 (95 % CI 1.28–4.13) times the odds of exceeding the highest threshold. Low socioeconomic status groups are more likely to drink at extreme levels, which may partially explain the alcohol harm paradox. Policies that address alcohol-related health inequalities need to consider extreme drinking levels in some sub-groups that may be associated with multiple markers of deprivation. This will require a more disaggregated understanding of drinking practices.