论文标题
相对对比度估计和治疗建议的推断
Relative Contrast Estimation and Inference for Treatment Recommendation
论文作者
论文摘要
当存在资源限制时，重要的是根据患者特征对治疗益处进行排名或估算益处。这有助于分配不同治疗方法的优先级。关于个性化治疗规则的大多数现有文献目标是绝对条件治疗效果差异作为收益的指标。但是，在某些情况下，相对差异可以更好地代表此类好处。在本文中，我们考虑建模有条件治疗效应之间形成量表不变的对比的相对差异。我们表明，所有规模不变的对比都是彼此的单调转换。因此，我们为特定的相对对比度设置一个单个索引模型。模型的可识别性通过直观$ L_2 $ norm限制在索引参数上实现。然后，我们通过半参数效率理论得出估计方程和有效分数。根据有效的分数及其变体，我们提出了一种两步方法，包括最大程度地减少双重稳健的损失函数和随后的一步效率增强程序，以实现效率结合。提供了仔细的理论和数值研究，以表明所提出的方法的优越性。
When there are resource constraints, it is important to rank or estimate treatment benefits according to patient characteristics. This facilitates prioritization of assigning different treatments. Most existing literature on individualized treatment rules targets absolute conditional treatment effect differences as the metric for benefits. However, there can be settings where relative differences may better represent such benefits. In this paper, we consider modeling such relative differences that form scale-invariant contrasts between conditional treatment effects. We show that all scale-invariant contrasts are monotonic transformations of each other. Therefore we posit a single index model for a particular relative contrast. Identifiability of the model is enforced via an intuitive $l_2$ norm constraint on index parameters. We then derive estimating equations and efficient scores via semiparametric efficiency theory. Based on the efficient score and its variant, we propose a two-step approach that consists of minimizing a doubly robust loss function and a subsequent one-step efficiency augmentation procedure to achieve efficiency bound. Careful theoretical and numerical studies are provided to show the superiority of the proposed approach.
