Department of Epidemiology and Biostatistics, The University of California, San Francisco, CA 94143, United States.
Department of Mathematics and Statistics, Portland State University, Portland, OR 97201, United States.
Biometrics. 2024 Oct 3;80(4). doi: 10.1093/biomtc/ujae110.
With the ever advancing of modern technologies, it has become increasingly common that the number of collected confounders exceeds the number of subjects in a data set. However, matching based methods for estimating causal treatment effect in their original forms are not capable of handling high-dimensional confounders, and their various modified versions lack statistical support and valid inference tools. In this article, we propose a new approach for estimating causal treatment effect, defined as the difference of the restricted mean survival time (RMST) under different treatments in high-dimensional setting for survival data. We combine the factor model and the sufficient dimension reduction techniques to construct propensity score and prognostic score. Based on these scores, we develop a kernel based doubly robust estimator of the RMST difference. We demonstrate its link to matching and establish the consistency and asymptotic normality of the estimator. We illustrate our method by analyzing a dataset from a study aimed at comparing the effects of two alternative treatments on the RMST of patients with diffuse large B cell lymphoma.
随着现代技术的不断进步，在一个数据集中文献中收集的混杂因素数量超过了研究对象的数量变得越来越常见。然而，基于匹配的方法在原始形式下无法处理高维混杂因素，而它们的各种修改版本缺乏统计支持和有效的推断工具。在本文中，我们提出了一种新的方法来估计因果治疗效果，该效果定义为在高维生存数据中不同治疗下受限平均生存时间 (RMST) 的差异。我们结合因子模型和充分降维技术来构建倾向评分和预后评分。基于这些评分，我们开发了一种基于核的 RMST 差异的双重稳健估计量。我们证明了它与匹配的联系，并建立了估计量的一致性和渐近正态性。我们通过分析一项旨在比较两种替代治疗方法对弥漫性大 B 细胞淋巴瘤患者 RMST 影响的研究数据集来说明我们的方法。
