论文标题
如何联系潜在的结果：在给定的指定部分相关下估计单个治疗效果
How to relate potential outcomes: Estimating individual treatment effects under a given specified partial correlation
论文作者
论文摘要
在大多数医学研究中，平均治疗效果用于评估治疗的表现。但是，精确医学需要了解个体治疗效果：在治疗和控制条件下，单位测量之间有什么区别？在大多数治疗效应研究中，由于没有共同观察到两个实验条件下的结果，因此无法进行此类答案。这使果推理的问题缺失了数据问题。我们建议通过在指定的部分相关性（SPC）下提出各个潜在结果来解决这个问题，从而允许异质治疗效果。我们在模拟中证明，我们所提出的方法对潜在结果的边际分布产生了有效的推论。我们强调，个体治疗效应的后验分布随不同指定的部分相关性而变化。该特性可用于研究不同相关规范下最佳治疗结果的敏感性。在HIV-1治疗数据的一个实际示例中，我们证明了所提出的方法对现实世界数据进行了普遍性。因此，在SPC下进行归类为研究对不完整数据的异质治疗效果的大量可能性，并进一步适应了个体治疗效应。
In most medical research, the average treatment effect is used to evaluate a treatment's performance. However, precision medicine requires knowledge of individual treatment effects: What is the difference between a unit's measurement under treatment and control conditions? In most treatment effect studies, such answers are not possible because the outcomes under both experimental conditions are not jointly observed. This makes the problem of causal inference a missing data problem. We propose to solve this problem by imputing the individual potential outcomes under a specified partial correlation (SPC), thereby allowing for heterogeneous treatment effects. We demonstrate in simulation that our proposed methodology yields valid inferences for the marginal distribution of potential outcomes. We highlight that the posterior distribution of individual treatment effects varies with different specified partial correlations. This property can be used to study the sensitivity of optimal treatment outcomes under different correlation specifications. In a practical example on HIV-1 treatment data, we demonstrate that the proposed methodology generalises to real-world data. Imputing under the SPC, therefore, opens up a wealth of possibilities for studying heterogeneous treatment effects on incomplete data and the further adaptation of individual treatment effects.
