论文标题
可视化连续变量对事件时间结果的（因果）效应
Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome
论文作者
论文摘要
可视化是传达任何旨在估计因果影响的研究结果的关键方面。在与事件时间结果的研究中，最受欢迎的可视化方法是描绘出感兴趣变量分层的生存曲线。当关注的变量是连续的时，无法使用此方法。简单的解决方法，例如对每个类别的连续协变量和绘制生存曲线进行分类，可能会导致对主要影响的误导性描述。取而代之的是，我们提出了一个新的图形，即生存区域图，以直接描述随着时间的流逝，并同时描绘了连续协变量的函数。该图利用基于合适的事实模型的G-Compuntion来获得相关的估计。通过使用G-Compunn，可以调整这些估计值，以使其在不付出的努力的情况下进行混淆，从而在标准因果可识别性假设下进行因果解释。如果未达到这些假设，则建议的图仍可用于描述非因果关系。我们使用来自大型德国观察性研究的数据来说明和将提议的图形与更简单的替代方法进行比较，该研究研究了踝臂指数对生存的影响。为了促进这些图的使用，我们还开发了CONTSURVPLOT R-package，其中包括本文讨论的所有方法。
Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variable of interest is continuous. Simple workarounds, such as categorizing the continuous covariate and plotting survival curves for each category, can result in misleading depictions of the main effects. Instead, we propose a new graphic, the survival area plot, to directly depict the survival probability over time and as a function of a continuous covariate simultaneously. This plot utilizes g-computation based on a suitable time-to-event model to obtain the relevant estimates. Through the use of g-computation, those estimates can be adjusted for confounding without additional effort, allowing a causal interpretation under the standard causal identifiability assumptions. If those assumptions are not met, the proposed plot may still be used to depict noncausal associations. We illustrate and compare the proposed graphics to simpler alternatives using data from a large German observational study investigating the effect of the Ankle Brachial Index on survival. To facilitate the usage of these plots, we additionally developed the contsurvplot R-package which includes all methods discussed in this paper.
