Individualized treatment rules (ITRs) for treatment recommendation is an important topic for precision medicine as not all beneficial treatments work well for all individuals. Interpretability is a desirable property of ITRs, as it helps practitioners make sense of treatment decisions, yet there is a need for ITRs to be flexible to effectively model complex biomedical data for treatment decision making. Many ITR approaches either focus on linear ITRs, which may perform poorly when true optimal ITRs are nonlinear, or black-box nonlinear ITRs, which may be hard to interpret and can be overly complex. This dilemma indicates a tension between interpretability and accuracy of treatment decisions. Here we propose an additive model-based nonlinear ITR learning method that balances interpretability and flexibility of the ITR. Our approach aims to strike this balance by allowing both linear and nonlinear terms of the covariates in the final ITR. Our approach is parsimonious in that the nonlinear term is included in the final ITR only when it substantially improves the ITR performance. To prevent overfitting, we combine cross-fitting and a specialized information criterion for model selection. Through extensive simulations, we show that our methods are data-adaptive to the degree of nonlinearity and can favorably balance ITR interpretability and flexibility. We further demonstrate the robust performance of our methods with an application to a cancer drug sensitive study.

翻译：个体化治疗规则（ITRs）用于治疗方案推荐是个性化医疗的重要课题，因为并非所有有效治疗方案都适用于所有个体。可解释性是ITRs的理想属性，有助于实践者理解治疗决策的逻辑，但同时也需要ITRs具备灵活性以有效建模复杂生物医学数据用于治疗决策。现有许多ITR方法要么聚焦于线性ITR模型（当真实最优ITR为非线性的可能表现不佳），要么采用黑箱式非线性ITR模型（可能难以解释且过度复杂）。这种困境体现了治疗决策可解释性与准确性之间的张力。本文提出一种基于加性模型的非线性ITR学习方法，平衡了ITR的可解释性与灵活性。我们的方法通过在最终ITR中同时允许协变量的线性项与非线性项来实现这种平衡。该方法具有简约性：仅当非线性项能显著提升ITR性能时才将其纳入最终模型。为防止过拟合，我们结合交叉拟合与专门设计的信息准则进行模型选择。通过大量仿真实验验证，我们的方法能够自适应数据非线性程度，并有效平衡ITR的可解释性与灵活性。我们进一步通过癌症药物敏感性研究实例展示了方法的稳健性能。