The personalized advantage index: Translating research on prediction into individualized treatment recommendations. A demonstration

Background: Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations. Objective: To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison. Method: Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units. Results: For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their "Optimal" treatment versus those assigned to their "Non-optimal" treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17-1.01). Conclusions: This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments. © 2014 DeRubeis et al

Costs of Change, Political Polarization, and Re-Election Hurdles

We develop and study a two-period model of political competition with office- and policymotivated candidates, in which (i) changes of policies impose costs on all individuals and (ii) such costs increase with the magnitude of the policy change. We show that there is an optimal positive level of costs of change that minimizes policy polarization and maximizes welfare. One interpretation of this finding is that societies with intermediate levels of conservatism achieve the highest welfare and the lowest polarization levels. We apply our model to the design of optimal re-election hurdles. In particular, we show that raising the vote-share needed for re-election above 50% weakly reduces policy polarization and tends to increase welfare. Furthermore, we identify circumstances where the optimal re-election hurdle is strictly larger than 50%