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.
翻译:在大多数医学研究中,平均治疗效果被用于评估治疗的性能。然而,精密医学要求了解个别治疗效果:在治疗和控制条件下,单位的测量方法有何区别?在大多数治疗效果研究中,这种答案是不可能的,因为两个实验条件下的结果都没有共同观察到。这使得因果推断问题成为缺失的数据问题。我们提议通过根据特定的部分相关关系(SPC)估算个人潜在结果来解决这一问题,从而允许不同治疗效果。我们在模拟中表明,我们提议的方法对潜在结果的边际分布产生有效的推论。我们强调,个别治疗效果的外表分布不同,而不同的特定部分关联性则不同。这种属性可用于研究不同相关规格下最佳治疗结果的敏感性。在艾滋病毒-1治疗数据的一个实例中,我们证明,拟议的方法对真实世界数据作了概括。因此,根据SPC进行推算,为研究对不完全数据的综合治疗影响和进一步调整个人治疗效果提供了大量的可能性。