Chen Baojiang, Yuan Ao, Qin Jing
Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, School of Public Health in Austin, Austin, Texas.
Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, District of Columbia.
Biometrics. 2022 Dec;78(4):1475-1488. doi: 10.1111/biom.13511. Epub 2021 Sep 7.
Personalized medicine allows individuals to choose the best fit of their treatments based on their characteristics through an individualized treatment regime. In this paper, we develop a pool adjacent violators algorithm-assisted learning method to find the optimal individualized treatment regime under the monotone single-index outcome gain model. The proposed estimator is more efficient than peers, and it is robust to the misspecification of the propensity score model or the baseline regression model. The optimal treatment regime is also robust to the misspecification of the functional form of the expected outcome gain model. Simulation studies verified our theoretical results. We also provide an estimate of the expected outcome gain model. Plotting the expected outcome gain versus an individual's characteristics index can visualize how significant the treatment effect is over the control. We apply the proposed method to an AIDS study.
个性化医疗允许个体通过个性化治疗方案，根据自身特征选择最适合自己的治疗方法。在本文中，我们开发了一种池相邻违规者算法辅助学习方法，以在单调单指标结果增益模型下找到最优个性化治疗方案。所提出的估计器比同类方法更有效，并且对倾向得分模型或基线回归模型的误设具有鲁棒性。最优治疗方案对预期结果增益模型的函数形式误设也具有鲁棒性。模拟研究验证了我们的理论结果。我们还提供了预期结果增益模型的估计。绘制预期结果增益与个体特征指数的关系图，可以直观显示治疗效果相对于对照的显著程度。我们将所提出的方法应用于一项艾滋病研究。
