Contrast weighted learning for robust optimal treatment rule estimation

Personalized medicine aims to tailor medical decisions based on patient-specific characteristics. Advances in data capturing techniques such as electronic health records dramatically increase the availability of comprehensive patient profiles, promoting the rapid development of optimal treatment rule (OTR) estimation methods. An archetypal OTR estimation approach is the outcome weighted learning, where OTR is determined under a weighted classification framework with clinical outcomes as the weights. Although outcome weighted learning has been extensively studied and extended, existing methods are susceptible to irregularities of outcome distributions such as outliers and heavy tails. Methods that involve modeling of the outcome are also sensitive to model misspecification. We propose a contrast weighted learning (CWL) framework that exploits the flexibility and robustness of contrast functions to enable robust OTR estimation for a wide range of clinical outcomes. The novel value function in CWL only depends on the pairwise contrast of clinical outcomes between patients irrespective of their distributional features and supports. The Fisher consistency and convergence rate of the estimated decision rule via CWL are established. We illustrate the superiority of the proposed method under finite samples using comprehensive simulation studies with ill-distributed continuous outcomes and ordinal outcomes. We apply the CWL method to two datasets from clinical trials on idiopathic pulmonary fibrosis and COVID-19 to demonstrate its real-world application.