Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany.
Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.
Int J Epidemiol. 2021 Mar 3;50(1):266-278. doi: 10.1093/ije/dyaa164.
The results of studies on observational associations may vary depending on the study design and analysis choices as well as due to measurement error. It is important to understand the relative contribution of different factors towards generating variable results, including low sample sizes, researchers' flexibility in model choices, and measurement error in variables of interest and adjustment variables.
We define sampling, model and measurement uncertainty, and extend the concept of vibration of effects in order to study these three types of uncertainty in a common framework. In a practical application, we examine these types of uncertainty in a Cox model using data from the National Health and Nutrition Examination Survey. In addition, we analyse the behaviour of sampling, model and measurement uncertainty for varying sample sizes in a simulation study.
All types of uncertainty are associated with a potentially large variability in effect estimates. Measurement error in the variable of interest attenuates the true effect in most cases, but can occasionally lead to overestimation. When we consider measurement error in both the variable of interest and adjustment variables, the vibration of effects are even less predictable as both systematic under- and over-estimation of the true effect can be observed. The results on simulated data show that measurement and model vibration remain non-negligible even for large sample sizes.
Sampling, model and measurement uncertainty can have important consequences for the stability of observational associations. We recommend systematically studying and reporting these types of uncertainty, and comparing them in a common framework.
观察性关联研究的结果可能因研究设计和分析选择以及由于测量误差而有所不同。了解导致结果变化的不同因素的相对贡献很重要，包括样本量小、研究人员在模型选择方面的灵活性，以及感兴趣变量和调整变量的测量误差。
我们定义了抽样、模型和测量不确定性，并扩展了效果振动的概念，以便在一个共同的框架中研究这三种类型的不确定性。在实际应用中，我们使用来自国家健康和营养检查调查的数据，在 Cox 模型中检查了这些类型的不确定性。此外，我们还在模拟研究中分析了不同样本量下抽样、模型和测量不确定性的行为。
所有类型的不确定性都与效应估计值的潜在大变异有关。在大多数情况下，感兴趣变量的测量误差会减弱真实效应，但偶尔也会导致高估。当我们同时考虑感兴趣变量和调整变量的测量误差时，由于可以观察到系统的低估和高估真实效应，因此效果的振动更加不可预测。模拟数据的结果表明，即使对于大样本量，测量和模型振动仍然不可忽略。
抽样、模型和测量不确定性可能对观察性关联的稳定性产生重要影响。我们建议系统地研究和报告这些类型的不确定性，并在共同的框架中进行比较。
