推断暴露混合物中的协同和拮抗相互作用。

IF 1.4 4区 数学 Q2 STATISTICS & PROBABILITY
Annals of Applied Statistics Pub Date : 2025-03-01 Epub Date: 2025-03-17 DOI:10.1214/24-aoas1948
Shounak Chattopadhyay, Stephanie M Engel, David Dunson
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引用次数: 0

摘要

人们对评估多重接触对人类健康的共同影响非常感兴趣。这通常被称为环境流行病学和毒理学中的混合物问题。传统上,研究一次只检查一种不同化学物质对健康的不利影响,但人们担心某些化学物质可能会共同作用,放大彼此的影响。这种放大被称为协同作用,而相互抑制作用的化学物质具有拮抗作用。目前评估化学混合物对健康影响的方法在建模中没有明确考虑协同作用或拮抗作用,而是侧重于参数或无约束的非参数剂量反应面建模。参数化的情况可能太不灵活,而非参数化的方法面临维度的诅咒,导致过度扭曲和不可解释的表面估计。我们提出了一种贝叶斯方法,该方法将响应面分解为可加性主效应和成对相互作用效应,然后检测协同和拮抗相互作用。还提供了每个交互组件的可变选择决策。该协同拮抗相互作用检测(SAID)框架通过模拟实验和NHANES数据应用,相对于现有方法进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
INFERRING SYNERGISTIC AND ANTAGONISTIC INTERACTIONS IN MIXTURES OF EXPOSURES.

There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse health effects of different chemicals one at a time, but there is concern that certain chemicals may act together to amplify each other's effects. Such amplification is referred to as synergistic interaction, while chemicals that inhibit each other's effects have antagonistic interactions. Current approaches for assessing the health effects of chemical mixtures do not explicitly consider synergy or antagonism in the modeling, instead focusing on either parametric or unconstrained nonparametric dose response surface modeling. The parametric case can be too inflexible, while nonparametric methods face a curse of dimensionality that leads to overly wiggly and uninterpretable surface estimates. We propose a Bayesian approach that decomposes the response surface into additive main effects and pairwise interaction effects and then detects synergistic and antagonistic interactions. Variable selection decisions for each interaction component are also provided. This Synergistic Antagonistic Interaction Detection (SAID) framework is evaluated relative to existing approaches using simulation experiments and an application to data from NHANES.

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来源期刊
Annals of Applied Statistics
Annals of Applied Statistics 社会科学-统计学与概率论
CiteScore
3.10
自引率
5.60%
发文量
131
审稿时长
6-12 weeks
期刊介绍: Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.
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