Causal effect estimation in survival analysis with high dimensional confounders.

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2024-10-03 DOI:10.1093/biomtc/ujae110
Fei Jiang, Ge Zhao, Rosa Rodriguez-Monguio, Yanyuan Ma
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引用次数: 0

Abstract

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.

具有高维度混杂因素的生存分析中的因果效应估计。
随着现代技术的不断进步,收集到的混杂因素数量超过数据集中受试者数量的情况越来越普遍。然而,基于配对的因果治疗效果估计方法的原始形式无法处理高维混杂因素,其各种修改版本也缺乏统计支持和有效的推断工具。在本文中,我们提出了一种估算因果治疗效果的新方法,即在高维生存数据环境下,将因果治疗效果定义为不同治疗下受限平均生存时间(RMST)之差。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。基于这些分数,我们开发了基于核的 RMST 差异双重稳健估计器。我们证明了它与匹配的联系,并建立了估计器的一致性和渐近正态性。我们通过分析一项研究的数据集来说明我们的方法,该研究旨在比较两种替代治疗方法对弥漫大 B 细胞淋巴瘤患者 RMST 的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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