高维个体化治疗规则估计指南。

IF 1.2 4区 数学
Philippe Boileau, Ning Leng, Sandrine Dudoit
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引用次数: 0

摘要

个性化治疗规则是精准医疗的基石,为患者的治疗决策提供信息,目标是优化患者的治疗结果。这些规则通常是患者治疗前协变量的未知函数,这意味着它们必须从临床或观察性研究数据中估计出来。已经开发了无数方法来学习这些规则,并且这些程序在具有中等数量协变量的传统渐近设置中明显成功。然而,这些方法在高维协变量设置中的有限样本性能(这在现代临床试验中越来越普遍 )尚未得到很好的表征。我们对最先进的个性化治疗规则估计器进行了全面的比较,在估计器的规则质量、可解释性和计算效率的基础上评估性能。考虑了具有连续结果和二元治疗分配的16个数据生成过程,反映了随机和观察性研究的多样性。我们总结了我们的发现,并提供简洁的建议,从业者需要估计个体化治疗规则在高维。由于个性化治疗规则估计器的可解释性较差,我们提出了一种新的预处理协变量过滤程序,基于最近的工作来揭示治疗效果修饰符。我们证明了它提高了估计器的规则质量和可解释性。所有代码都是公开的,便于修改和扩展我们的模拟研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Guidance on individualized treatment rule estimation in high dimensions.

Individualized treatment rules, cornerstones of precision medicine, inform patient treatment decisions with the goal of optimizing patient outcomes. These rules are generally unknown functions of patients' pre-treatment covariates, meaning they must be estimated from clinical or observational study data. Myriad methods have been developed to learn these rules, and these procedures are demonstrably successful in traditional asymptotic settings with moderate number of covariates. The finite-sample performance of these methods in high-dimensional covariate settings, which are increasingly the norm in modern clinical trials, has not been well characterized, however. We perform a comprehensive comparison of state-of-the-art individualized treatment rule estimators, assessing performance on the basis of the estimators' rule quality, interpretability, and computational efficiency. Sixteen data-generating processes with continuous outcomes and binary treatment assignments are considered, reflecting a diversity of randomized and observational studies. We summarize our findings and provide succinct advice to practitioners needing to estimate individualized treatment rules in high dimensions. Owing to individualized treatment rule estimators' poor interpretability, we propose a novel pre-treatment covariate filtering procedure based on recent work for uncovering treatment effect modifiers. We show that it improves estimators' rule quality and interpretability. All code is made publicly available, facilitating modifications and extensions to our simulation study.

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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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