A Bayesian approach for estimating the partial potential impact fraction with exposure measurement error under a main study/internal validation design

Individual differences in the effects of the ACTION-PAC intervention: an application of personalized medicine in the prevention and treatment of obesity

There is an increased interest in the use of personalized medicine approaches in the prevention or treatment of obesity, however, few studies have used these approaches to identify individual differences in treatment effects. The current study demonstrates the use of the predicted individual treatment effects framework to test for individual differences in the effects of the ACTION-PAC intervention, which targeted the treatment and prevention of obesity in a high school setting. We show how methods for personalized medicine can be used to test for significant individual differences in responses to an intervention and we discuss the potential and limitations of these methods. In our example, 25% of students in the preventive intervention, were predicted to have their BMI z-score reduced by 0.39 or greater, while at other end of the spectrum, 25% were predicted to have their BMI z-score increased by 0.09 or more. In this paper, we demonstrate and discuss the process of using methods for personalized medicine with interventions targeting adiposity and discuss the lessons learned from this application. Ultimately, these methods have the potential to be useful for clinicians and clients in choosing between treatment options, however they are limited in their ability to help researchers understand the mechanisms underlying these predictions.