Uplift modeling for clinical trial data
Traditional classification methods predict the class probability distribution conditional on a set of predictor variables. Uplift modeling, in contrast, tries to predict the difference between class probabilities in the treatment group (on which some action has been taken) and the control group (not subjected to the action) such that the model predicts the net effect of the action. Such an approach seems to be well suited to analysis of clinical trial data and to allow for discovering groups of patients for which the treatment is most beneficial. One of the purposes of this paper is to verify this claim experimentally. Additionally, we present an approach to uplift modeling which allows for application of standard probabilistic classification models, such as logistic regression, in the uplift setting. Further, we extend the approach such that standard classification models built on the treatment and control datasets can be incorporated in a manner similar to semisupervised learning in order to improve prediction accuracy. The usefulness of both approaches has been verified experimentally on publicly available clinical trial data.