Bridging the gap: predicting the critical window at intake in contingency management

Abstract

Contingency management (CM) is a psychosocial treatment used to improve a number of socially significant behaviors. Its efficacy has been demonstrated many times over in a variety of treatment contexts, and efforts to demonstrate its effectiveness are underway at the state level. Development of useful predictive modeling has lagged far behind other technological advancements in methodology for improving treatment outcomes. Based on these other technological advancements it is well known that modifying treatment parameters (e.g., incentive magnitude) can result in better treatment outcomes, yet, to my knowledge, no studies have relied on predictive models to provide additional supports to those who may not be successful under default treatment parameters. In the present paper I propose and validate a framework for predicting early success in CM treatment using a large CM dataset consisting of over 800 participants. Within that framework I develop models to predict CM treatment outcomes using only data from intake questionnaires and the prediction of early treatment success. The best performing model for predicting early treatment success achieved a receiver operating characteristic area under the curve (ROC-AUC) score of 0.83. Using that model I predicted continuous abstinence of at least 8 and at least 12, with ROC-AUC scores of 0.81 and 0.77 respectively