Using Target Efficiency to Select Program Participants and Risk-Factor Models: An Application to Child Mental Health Interventions for Preventing Future Crime

Statistical risk factor models are often proposed for screening high-risk children to participate in early intervention programs. Recent contributions to the program evaluation literature demonstrate the need for incorporating judgments about relative importance of false positives versus false negatives in screening. This paper formalizes these judgments as commensurable economic costs and benefits and applies them to demonstrate an approach to participant selection motivated by the standard cost-benefit criterion of maximizing expected net benefits. Implications of this approach are explored using data from a mental health prevention trial. We illustrate the response of expected net benefits to the choice of a selection risk level, the sensitivity of the optimal selection risk level to per participant cost/benefit magnitudes, and the use of the target-efficiency approach for choosing among alternative risk-factor models. Several strategies that directly incorporate expected net benefit maximization as a criterion in the model estimation process are also examined.