Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
CPT Pharmacometrics Syst Pharmacol. 2021 Nov;10(11):1433-1443. doi: 10.1002/psp4.12715. Epub 2021 Oct 30.
Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE-informed understanding can critically guide physicians to individualize the medical treatment for a certain disease. However, HTE analysis has not been widely recognized and used, even given the explosive increase of data availability attributed to the arrival of the Big Data era. Part of the reason behind its underuse is that data are often of high dimension and high complexity, which pose significant challenges for applying conventional HTE analysis methods. To meet these challenges, a newly developed causal forest HTE method has been derived from the random forest machine-learning algorithm. We conducted a systematic performance evaluation for the causal forest method against the conventional two-step method by simulating scenarios with different levels of complexity for the analysis. Our results show that causal forest outperforms the conventional HTE method in assessing treatment effect, especially when data are complex (e.g., nonlinear) and high dimensional, suggesting that causal forest is a promising tool for real-world applications of HTE analysis.
异质处理效应 (HTE) 分析侧重于研究人群中个体或亚组的治疗效果差异。例如，对 HTE 的深入了解可以帮助医生为特定疾病的患者提供个性化的医疗服务。然而，尽管大数据时代的数据可用性呈爆炸式增长，但 HTE 分析并未得到广泛认可和应用。其应用不足的部分原因在于数据通常具有高维性和复杂性，这给应用传统 HTE 分析方法带来了重大挑战。为了应对这些挑战，一种新的因果森林 HTE 方法已经从随机森林机器学习算法中衍生出来。我们通过模拟不同复杂程度的分析场景，对因果森林方法与传统两步法的系统性能进行了评估。结果表明，因果森林在评估治疗效果方面优于传统的 HTE 方法，尤其是在数据复杂（例如非线性）和高维的情况下，这表明因果森林是 HTE 分析在实际应用中的一种很有前途的工具。
