C-1 USING BAYESIAN METHODS TO SYNTHESISE EVIDENCE ON THE EFFICACY OF ELECTRONIC AIDS TO SMOKING CESSATION

Monday, October 25, 2010: 1:30 PM
Grand Ballroom West (Sheraton Centre Toronto Hotel)
Jason Madan, MA, MSc, PhD, University of Bristol, Bristol, United Kingdom and Nicky J. Welton, PhD, Bristol University, Bristol, United Kingdom

Purpose: To estimate the efficacy of electronic aids to smoking cessation, as an input into a cost-effectiveness analysis of such interventions.

Method: A prior systematic review identified studies evaluating electronic aids to smoking cessation, (websites, computer-based tailored advice, chat rooms, email / SMS communications, etc). A classification system was developed for these aids with five levels, ascending from single-component interventions providing generic advice (e.g. static website) to interventions with multiple components providing tailored feedback through several channels (e.g. interactive website + email + chat room). To synthesise this evidence and estimate class-level treatment effects, a Bayesian mixed-treatment comparison was constructed. This involved fitting a proportional-hazard Weibull survival model on sustained abstinence, as this was the main outcome of interest for the cost-effectiveness model. For some treatment classes, evidence on sustained abstinence was lacking, but point abstinence rates were available. A log-odds treatment effect was fitted to the latter type of outcome, along with a correlation structure between treatment effects on the two outcome types. This allowed treatment effects on sustained abstinence to be estimated for all intervention classes.

Result: 51 studies were included in the analysis, with 127 arms. 62 arms reported sustained abstinence, of which 51 arms also reported point abstinence. The mean shape for the Weibull survival model was 0.18 (95% credible interval (CrI) 0.09-0.32), which was consistent with the hypothesis that quitting is hardest initially and becomes easier to sustain with time.  There was an inverse relationship between the mean hazard ratio and the class of treatment, with estimates ranging from 1.15 (95% CrI 0.87-1.45) for the class one hazard ratio to 0.81 (95% CrI 0.68-0.93) for the class five hazard ratio.

Conclusion: Bayesian methods allow for uncertainty in treatment effects to be quantified whilst incorporating treatment classes, multiple outcomes, and repeated measurements. Application to data on electronic smoking cessation aids demonstrated that such interventions are likely to improve sustained abstinence, and that increased intensity may lead to better outcomes. Further studies are required to determine the incremental benefits of more intensive electronic interventions; the design of these trials should be informed by value of information analyses.