论文标题
杂种审查的分位数回归森林评估异质效应
Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects
论文作者
论文摘要
在许多应用中，对审查响应变量的异质治疗效果引起了主要兴趣，并且自然评估不同分位数的效果（例如中位数）。大量的潜在效应修饰符，治疗效果的未知结构以及正确审查的存在构成了重大挑战。在本文中，我们开发了一种称为混合审查的分位数回归森林（HCQRF）的混合森林方法，以评估随着高维变量而变化的异质效应。混合估计方法利用随机森林和审查的分位数回归。我们提出了一个双重加权的估计程序，该程序由重新分布的重量组成，以处理审查和自适应的最接近的邻居重量，从森林中得出来处理高维效应函数。我们提出了一个可变的重要性分解，以衡量变量对治疗效果函数的影响。广泛的仿真研究表明了HCQRF的功效和稳定性。模拟研究的结果还使我们相信了可变重要性分解的有效性。我们将HCQRF应用于结直肠癌的临床试验。我们实现了对治疗效果的有见地的估计和有意义的可变重要性结果。变量重要性的结果也证实了分解的必要性。
In many applications, heterogeneous treatment effects on a censored response variable are of primary interest, and it is natural to evaluate the effects at different quantiles (e.g., median). The large number of potential effect modifiers, the unknown structure of the treatment effects, and the presence of right censoring pose significant challenges. In this paper, we develop a hybrid forest approach called Hybrid Censored Quantile Regression Forest (HCQRF) to assess the heterogeneous effects varying with high-dimensional variables. The hybrid estimation approach takes advantage of the random forests and the censored quantile regression. We propose a doubly-weighted estimation procedure that consists of a redistribution-of-mass weight to handle censoring and an adaptive nearest neighbor weight derived from the forest to handle high-dimensional effect functions. We propose a variable importance decomposition to measure the impact of a variable on the treatment effect function. Extensive simulation studies demonstrate the efficacy and stability of HCQRF. The result of the simulation study also convinces us of the effectiveness of the variable importance decomposition. We apply HCQRF to a clinical trial of colorectal cancer. We achieve insightful estimations of the treatment effect and meaningful variable importance results. The result of the variable importance also confirms the necessity of the decomposition.
