反概率权重偏差-方差权衡中变量选择的可视化工具。
Visualization tool of variable selection in bias-variance tradeoff for inverse probability weights.
机构信息
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal.
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal; Department of Medicine, McGill University, Montreal, QC, Canada.
出版信息
Ann Epidemiol. 2020 Jan;41:56-59. doi: 10.1016/j.annepidem.2019.12.006. Epub 2019 Dec 13.
PURPOSE
Inversed probability weighted (IPW) estimators are commonly used to adjust for time-fixed or time-varying confounders. However, in high-dimensional settings, including all identified confounders may result in unstable weights leading to higher variance. We aimed to develop a visualization tool demonstrating the impact of each confounder on the bias and variance of IPW estimates, as well as the propensity score overlap.
METHODS
A SAS macro was developed for this visualization tool and we demonstrate how this tool can be used to identify potentially problematic confounders of the association of statin use after myocardial infarction on one-year mortality in a plasmode simulation study using a cohort of 39,792 patients from the UK (1998-2012).
RESULTS
Through the tool's output, we can identify problematic confounders (two instrumental variables) and important confounders by comparing the estimated psuedo MSE with that from the fully adjusted model and propensity score overlap plot.
CONCLUSION
Our results suggest that the analytic impact of all confounders should be considered carefully when fitting IPW estimators.
目的
逆概率加权（IPW）估计器常用于调整时间固定或随时间变化的混杂因素。然而，在高维环境中，包括所有已确定的混杂因素可能会导致权重不稳定，从而导致更高的方差。我们旨在开发一种可视化工具，以展示每个混杂因素对 IPW 估计的偏差和方差的影响，以及倾向评分重叠。
方法
我们开发了一个用于此可视化工具的 SAS 宏，并演示了如何使用该工具来识别在 UK（1998-2012 年）的 39792 名患者队列中，使用血浆模型模拟研究，心肌梗死后使用他汀类药物与一年死亡率的关联中潜在的有问题的混杂因素。
结果
通过工具的输出，我们可以通过比较估计的伪均方误差与完全调整模型和倾向评分重叠图的误差，来识别有问题的混杂因素（两个工具变量）和重要的混杂因素。
结论
我们的结果表明，在拟合 IPW 估计器时，应仔细考虑所有混杂因素的分析影响。
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