CoCoA: conditional correlation models with association size.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Danni Tu, Bridget Mahony, Tyler M Moore, Maxwell A Bertolero, Aaron F Alexander-Bloch, Ruben Gur, Dani S Bassett, Theodore D Satterthwaite, Armin Raznahan, Russell T Shinohara
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引用次数: 0

Abstract

Many scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose a conditional correlation model with association size, a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semiparametric estimators adapted from genomic studies and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modeling and partitioned correlation analyses.

CoCoA:具有关联大小的条件相关模型。
许多科学问题都可以表述为条件相关性假设。例如,在认知和体能测试中,速度和准确性之间的权衡促使人们把这两个变量放在一起研究。一个自然的问题是,速度与准确性之间的耦合是否取决于其他变量,如持续注意力。传统的回归技术是根据协变量和结果建立模型,不足以研究第三个变量对速度和准确性之间对称关系的影响。为此,我们提出了一个带有关联大小的条件相关模型,这是一个基于似然法的统计框架,用于估计速度和准确性之间作为附加变量函数的条件相关性。我们提出了新的关联大小测量方法,该方法类似于相关尺度上的效应大小,同时对混杂变量进行了调整。在模拟研究中,我们比较了基于似然法的条件相关性估计值和从基因组研究中改编而来的半参数估计值,发现前者在理想设置和模型假设失当的情况下都能获得较低的偏差和方差。利用费城神经发育队列的神经认知数据,我们证明了在控制年龄的情况下,更强的持续注意力与复杂推理任务中更强的速度-准确性耦合相关。通过将条件相关性作为感兴趣的结果,我们的模型为传统回归建模和分区相关性分析提供了补充见解。
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
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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