From intermediate to final behavioral endpoints; Modeling cognitions in (cost-)effectiveness analyses in health promotion
Prenger, Rilana (2012) From intermediate to final behavioral endpoints; Modeling cognitions in (cost-)effectiveness analyses in health promotion. thesis.
|Abstract:||Cost-effectiveness analyses (CEAs) are considered an increasingly important tool in health promotion and psychology. In health promotion adequate effectiveness data of innovative interventions are often lacking. In case of many promising interventions the available data are inadequate for CEAs due to a variable follow-up length or a lack of validated behavioral endpoints. Yet, in many of these cases effects on cognitive variables, such as intention and self-efficacy, are available. Modeling of cognitive parameters provide a way to overcome variations between studies, by estimating the required behavioral endpoints for use in CEAs. The research described in this thesis was initiated to develop a method to include partial behavior change in CEA by modeling cognitive parameters of behavior change. It aimed to accomplish two goals. First, to provide a method that can give more insight in long term effectiveness of behavioral interventions, because it provides a way to look beyond measured (intermediate) endpoints in available data (by predicting them). And second, to develop a method that can contribute to the standardization of CEAs of behavioral interventions, as it will provide a technology to model from varying (cognitive of behavioral) endpoints to a single estimated endpoint of behavior change.
The method described consists of several steps. First, for considering relevant cognitions of behavior change, time-varying analyses have to be applied, which has shown to be suitable for commonly used intervention designs. Second, transition probabilities between cognitive intermediate states and behavior change should be obtained, preferably from the original data or from literature. Third, costs have to be extrapolated to a future time period and finally, a predictive model can be constructed based on the future costs and effects to estimate future cost-effectiveness results. Ultimately, modeling cognitive parameters to predict behavior change at some future endpoint may have important implications for health policy and health behavior research in particular.
Faculty of Behavioural, Management and Social sciences (BMS)
|Link to this item:||http://purl.utwente.nl/publications/80538|
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