Real-world data (RWD) gains growing interests to provide a representative sample of the population for selecting the optimal treatment options. However, existing complex black box methods for estimating individualized treatment rules (ITR) from RWD have problems in interpretability and convergence. Providing an interpretable and sparse ITR can be used to overcome the limitation of existing methods. We developed an algorithm using Adaptive LASSO to predict optimal interpretable linear ITR in the RWD. To encourage sparsity, we obtain an ITR by minimizing the risk function with various types of penalties and different methods of contrast estimation. Simulation studies were conducted to select the best configuration and to compare the novel algorithm with the existing state-of-the-art methods. The proposed algorithm was applied to RWD to predict the optimal interpretable ITR. Simulations show that adaptive LASSO had the highest rates of correctly selected variables and augmented inverse probability weighting with Super Learner performed best for estimating treatment contrast. Our method had a better performance than causal forest and R-learning in terms of the value function and variable selection. The proposed algorithm can strike a balance between the interpretability of estimated ITR (by selecting a small set of important variables) and its value.

翻译：真实世界数据（RWD）因其能提供具有人群代表性的样本以选择最优治疗方案而日益受到关注。然而，现有基于RWD估计个体化治疗规则（ITR）的复杂黑箱方法存在可解释性与收敛性问题。提供可解释且稀疏的ITR可克服现有方法的局限性。我们开发了一种采用自适应LASSO的算法，用于预测RWD中最优可解释线性ITR。为增强稀疏性，我们通过最小化具有多种惩罚类型及不同对照估计方法的风险函数来获得ITR。通过模拟研究筛选最佳配置，并将新算法与现有先进方法进行比较。所提算法应用于RWD以预测最优可解释ITR。模拟结果表明：自适应LASSO具有最高的变量正确选择率，而结合超级学习器的增强逆概率加权法在治疗对照估计中表现最优。在价值函数和变量选择方面，本方法优于因果森林与R学习方法。所提算法能在估计ITR的可解释性（通过选择少量重要变量）与其价值之间取得平衡。