Agier Lydiane, Portengen Lützen, Chadeau-Hyam Marc, Basagaña Xavier, Giorgis-Allemand Lise, Siroux Valérie, Robinson Oliver, Vlaanderen Jelle, González Juan R, Nieuwenhuijsen Mark J, Vineis Paolo, Vrijheid Martine, Slama Rémy, Vermeulen Roel
Team of Environmental Epidemiology, Inserm, CNRS, University Grenoble-Alpes, IAB (institute for Advanced Biosciences), Grenoble, France.
Environ Health Perspect. 2016 Dec;124(12):1848-1856. doi: 10.1289/EHP172. Epub 2016 May 24.
The exposome constitutes a promising framework to improve understanding of the effects of environmental exposures on health by explicitly considering multiple testing and avoiding selective reporting. However, exposome studies are challenged by the simultaneous consideration of many correlated exposures.
We compared the performances of linear regression-based statistical methods in assessing exposome-health associations.
In a simulation study, we generated 237 exposure covariates with a realistic correlation structure and with a health outcome linearly related to 0 to 25 of these covariates. Statistical methods were compared primarily in terms of false discovery proportion (FDP) and sensitivity.
On average over all simulation settings, the elastic net and sparse partial least-squares regression showed a sensitivity of 76% and an FDP of 44%; Graphical Unit Evolutionary Stochastic Search (GUESS) and the deletion/substitution/addition (DSA) algorithm revealed a sensitivity of 81% and an FDP of 34%. The environment-wide association study (EWAS) underperformed these methods in terms of FDP (average FDP, 86%) despite a higher sensitivity. Performances decreased considerably when assuming an exposome exposure matrix with high levels of correlation between covariates.
Correlation between exposures is a challenge for exposome research, and the statistical methods investigated in this study were limited in their ability to efficiently differentiate true predictors from correlated covariates in a realistic exposome context. Although GUESS and DSA provided a marginally better balance between sensitivity and FDP, they did not outperform the other multivariate methods across all scenarios and properties examined, and computational complexity and flexibility should also be considered when choosing between these methods. Citation: Agier L, Portengen L, Chadeau-Hyam M, Basagaña X, Giorgis-Allemand L, Siroux V, Robinson O, Vlaanderen J, González JR, Nieuwenhuijsen MJ, Vineis P, Vrijheid M, Slama R, Vermeulen R. 2016. A systematic comparison of linear regression-based statistical methods to assess exposome-health associations. Environ Health Perspect 124:1848-1856; http://dx.doi.org/10.1289/EHP172.
暴露组构成了一个很有前景的框架，通过明确考虑多重检验并避免选择性报告，能更好地理解环境暴露对健康的影响。然而，暴露组研究面临着同时考虑许多相关暴露因素的挑战。
我们比较了基于线性回归的统计方法在评估暴露组与健康关联方面的性能。
在一项模拟研究中，我们生成了237个具有实际相关结构的暴露协变量，以及一个与其中0至25个协变量线性相关的健康结局。主要根据错误发现比例（FDP）和敏感性对统计方法进行比较。
在所有模拟设置中，平均而言，弹性网络和稀疏偏最小二乘回归的敏感性为76%，FDP为44%；图形单元进化随机搜索（GUESS）和删除/替换/添加（DSA）算法的敏感性为81%，FDP为34%。环境全关联研究（EWAS）尽管敏感性较高，但在FDP方面（平均FDP为86%）表现不如这些方法。当假设暴露组暴露矩阵中协变量之间具有高度相关性时，性能会大幅下降。
暴露因素之间的相关性是暴露组研究面临的一个挑战，本研究中所研究的统计方法在现实的暴露组背景下有效区分真正的预测因素和相关协变量的能力有限。尽管GUESS和DSA在敏感性和FDP之间提供了略好的平衡，但在所有考察的场景和特性中，它们并没有优于其他多变量方法，在选择这些方法时还应考虑计算复杂性和灵活性。引文：阿吉尔L，波特根L，沙多-海姆M，巴萨尼亚X，乔吉斯-阿勒曼德L，西鲁克斯V，罗宾逊O，弗兰德伦J，冈萨雷斯JR，纽温惠森MJ，维奈斯P，弗里赫德M，斯拉马R，韦尔梅伦R。2016年。基于线性回归的统计方法评估暴露组与健康关联的系统比较。《环境健康展望》124：1848 - 1856；http://dx.doi.org/10.1289/EHP172 。
