Major-Smith Daniel, Dvořák Tadeáš, Elhakeem Ahmed, Lawlor Deborah A, Tilling Kate, Smith Andrew D A C
MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
medRxiv. 2023 Jan 25:2023.01.24.23284935. doi: 10.1101/2023.01.24.23284935.
Structured life course modelling approaches (SLCMA) have been developed to understand how exposures across the lifespan relate to later health, but have primarily been restricted to single exposures. As multiple exposures can jointly impact health, here we: i) demonstrate how to extend SLCMA to include exposure interactions; ii) conduct a simulation study investigating the performance of these methods; and iii) apply these methods to explore associations of access to green space, and its interaction with socioeconomic position, with child cardiometabolic health.
We used three methods, all based on lasso regression, to select the most plausible life course model: visual inspection, information criteria and cross-validation. The simulation study assessed the ability of these approaches to detect the correct interaction term, while varying parameters which may impact power (e.g., interaction magnitude, sample size, exposure collinearity). Methods were then applied to data from a UK birth cohort.
There were trade-offs between false negatives and false positives in detecting the true interaction term for different model selection methods. Larger sample size, lower exposure collinearity, centering exposures, continuous outcomes and a larger interaction effect all increased power. In our applied example we found little-to-no association between access to green space, or its interaction with socioeconomic position, and child cardiometabolic outcomes.
Incorporating interactions between multiple exposures is an important extension to SLCMA. The choice of method depends on the researchers' assessment of the risks of under- vs over-fitting. These results also provide guidance for improving power to detect interactions using these methods.
已开发出结构化生命历程建模方法（SLCMA）来理解一生中的暴露因素与后期健康之间的关系，但主要限于单一暴露因素。由于多种暴露因素可共同影响健康，因此我们在此：i）展示如何扩展SLCMA以纳入暴露因素的相互作用；ii）进行一项模拟研究，调查这些方法的性能；iii）应用这些方法来探索绿地接触及其与社会经济地位的相互作用与儿童心脏代谢健康之间的关联。
我们使用了三种均基于套索回归的方法来选择最合理的生命历程模型：目视检查、信息准则和交叉验证。模拟研究评估了这些方法在检测正确的相互作用项方面的能力，同时改变可能影响检验效能的参数（例如，相互作用强度、样本量、暴露因素共线性）。然后将这些方法应用于来自英国一个出生队列的数据。
在检测不同模型选择方法的真实相互作用项时，假阴性和假阳性之间存在权衡。更大的样本量、更低的暴露因素共线性、对暴露因素进行中心化处理、连续结局以及更大的相互作用效应均增加了检验效能。在我们的应用实例中，我们发现绿地接触或其与社会经济地位的相互作用与儿童心脏代谢结局之间几乎没有关联。
纳入多种暴露因素之间的相互作用是SLCMA的一项重要扩展。方法的选择取决于研究人员对欠拟合与过拟合风险的评估。这些结果也为提高使用这些方法检测相互作用的检验效能提供了指导。
