评估处理效应异质性对判别性的影响
Evaluating the influence of treatment-effect heterogeneity on discrimination
AI总结：
本研究评估处理效应异质性对条件平均处理效应模型判别能力的影响，发现不同判别指标对异质性的要求不同，获益集中度在异质性极低时也能表现出完美判别。
AI中文摘要：
在个性化医疗中，分析处理效应的异质性至关重要，可用于识别哪些患者将从特定治疗中获益。用于指导治疗决策的条件平均处理效应（CATE）模型的性能可通过多种方式评估，其中一项重要指标是模型有效区分治疗获益者与非获益者的能力。尽管已有许多方法和算法被提出用于开发条件平均处理效应模型和个体化治疗规则，但针对人群潜在处理效应分布所能达到的判别能力，目前了解甚少。本研究针对一组20种具有不同平均处理效应和异质性水平的分布，计算了在 oracle CATE（理想条件平均处理效应）下可达到的判别能力。评估涵盖的判别指标包括：获益的c统计量、获益集中度以及人群平均处方效应（PAPE）。结果显示，本研究采用的三个指标并不需要相同水平的处理效应异质性即可达到高判别结果。值得注意的是，要获得高获益c统计量和PAPE值，需要比获得高获益集中度值更大的异质性。这三个指标在不同分布中的表现差异显著，例如，获益集中度可在处理效应异质性可忽略不计的场景中指示完美判别。
英文摘要：
Analyzing the heterogeneity of treatment effects is crucial in personalized medicine to identify which patients will benefit from specific treatments. The performance of a conditional average treatment effects model to guide treatment decisions can be assessed in different ways, with an important one being the model's ability to effectively discriminate between individuals who benefit from the treatment and those who do not. While many methods and algorithms have been proposed to develop conditional average treatment effects models and individualized treatment rules, little is known about the discriminative ability that can be achieved according to the population's underlying distribution of treatment effects. In this work, we computed the discrimination that can be achieved under oracle CATE for a panel of 20 distributions with varying average treatment effects and levels of heterogeneity. The assessment included the following discrimination metrics: the c-statistic for benefit, the concentration of benefit, and the population average prescriptive effect (PAPE). Results showed that the three metrics employed in this study did not require the same levels of treatment effect heterogeneity to lead to high discrimination results. Notably, achieving high c-statistic for benefit and PAPE values required greater heterogeneity than obtaining high concentration of benefit values. The three metrics considered behave very differently across the distributions. For instance, the concentration of benefit can indicate perfect discrimination in settings with negligible treatment-effects heterogeneity.
