We propose a new modeling and estimation approach to select the optimal treatment regime from different options through constructing a robust estimating equation. The method is protected against misspecification of the propensity score model, the outcome regression model for the non-treated group, or the potential non-monotonic treatment difference model. Our method also allows residual errors to depend on covariates. A single index structure is incorporated to facilitate the nonparametric estimation of the treatment difference. We then identify the optimal treatment through maximizing the value function. Theoretical properties of the treatment assignment strategy are established. We illustrate the performance and effectiveness of our proposed estimators through extensive simulation studies and a real dataset on the effect of maternal smoking on baby birth weight.
翻译:我们建议采用新的模型和估算方法,通过构建一个稳健的估计方程式,从不同选项中选择最佳治疗制度;该方法受到保护,避免偏好性分数模型、未治疗群体结果回归模型或潜在的非血压治疗差异模型有误;我们的方法还允许遗留错误取决于共差;我们采用了单一的指数结构,以便利对治疗差异进行非参数性估计;然后我们通过最大限度地扩大价值功能确定最佳治疗办法;确定了治疗分配战略的理论特性;我们通过广泛的模拟研究和关于孕产妇吸烟对婴儿出生体重的影响的实际数据集,说明了我们提议的估算员的性能和有效性。