Dealing with missing behavioral endpoints in health promotion research by modeling cognitive parameters in cost-effectiveness analyses of behavioral interventions: a validation study
Research output: Contribution to journal › Article › Academic › peer-review
Cost-effectiveness analyses (CEAs) of behavioral interventions typically use physical outcome criteria. However, any progress in cognitive antecedents of behavior change may be seen as a beneficial outcome of an intervention. The aim of this study is to explore the feasibility and validity of incorporating cognitive parameters of behavior change in CEAs.
The CEA from a randomized controlled trial on smoking cessation was reanalyzed. First, relevant cognitive antecedents of behavior change in this dataset were identified. Then, transition probabilities between combined states of smoking and cognitions at 6 weeks and corresponding 6 months smoking status were obtained from the dataset. These rates were extrapolated to the period from 6 to 12 months in a decision analytic model. Simulated results were compared with the 12 months' observed cost-effectiveness results.
Self-efficacy was the strongest time-varying predictor of smoking cessation. Twelve months' observed CEA results for the multiple tailoring intervention versus usual care showed (sic) 3188 had to be paid for each additional quitter versus (sic) 10,600 in the simulated model.
The simulated CEA showed largely similar but somewhat more conservative results. Using self-efficacy to enhance the estimation of the true behavioral outcome seems a feasible and valid way to estimate future cost-effectiveness.
- cost-effectiveness analyses, cognitions, behavior change, modeling, self-efficacy, LATENT TRANSITION ANALYSIS, SMOKING-CESSATION, SELF-EFFICACY, PLANNED BEHAVIOR, RELAPSE, PREVENTION