Advances in the multiphase optimization strategy (MOST) have suggested a new approach, decision analysis for intervention value efficiency (DAIVE), for selecting an optimized intervention based on the results of a factorial optimization trial. The new approach opens possibilities to select optimized interventions based on multiple valued outcomes. We applied DAIVE to identify an optimized information leaflet intended to support eventual adherence to adjuvant endocrine therapy for women with breast cancer. We used empirical performance data for five candidate leaflet components on three hypothesized antecedents of adherence: beliefs about the medication, objective knowledge about AET, and satisfaction with medication information. Using data from a 25 factorial trial (n = 1603), we applied the following steps: (i) We used Bayesian factorial analysis of variance to estimate main and interaction effects for the five factors on the three outcomes. (ii) We used posterior distributions for main and interaction effects to estimate expected outcomes for each leaflet version (32 total). (iii) We scaled and combined outcomes using a linear value function with predetermined weights indicating the relative importance of outcomes. (iv) We identified the leaflet that maximized the value function as the optimized leaflet, and we systematically varied outcome weights to explore robustness. The optimized leaflet included two candidate components, side-effects, and patient input, set to their higher levels. Selection was generally robust to weight variations consistent with the initial preferences for three outcomes. DAIVE enables selection of optimized interventions with the best-expected performance on multiple outcomes.
Keywords: Bayesian decision analytics; breast cancer; decision-making; factorial optimization trial; intervention optimization; multiphase optimization strategy.
Intervention optimization involves using data from an optimization trial to select the combination of intervention components that are expected to successfully balance effectiveness (i.e. improving an outcome in the desired direction) with efficiency (i.e. producing a good outcome without wasting resources). Recently, a new method for selecting optimized interventions has been proposed that has a number of advantages, including the ability to use empirical information about more than one outcome variable of interest. Here, we applied this new method to identify an optimized information leaflet designed to support eventual medication adherence in women with breast cancer, using empirical information about three outcome variables that are thought to be important for later medication adherence: beliefs about the medication, objective knowledge about the medication, and satisfaction with the leaflet information. When we let beliefs about the medication be most important; knowledge about the medication to be half as important as beliefs; and satisfaction with information to be half as important as knowledge, the optimized leaflet included enhanced information about side-effects and photos and quotes from women with breast cancer. This decision remained generally the same when we systematically varied the weights used to give outcomes their relative importance.
© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Behavioral Medicine.