Visualization tool of variable selection in bias–variance tradeoff for inverse probability weights

• Published in: Annals of epidemiology 2020-01, Vol.41, p.56-59
• Main Authors: Yu, Ya-Hui, Filion, Kristian B., Bodnar, Lisa M., Brooks, Maria M., Platt, Robert W., Himes, Katherine P., Naimi, Ashley I.
• Format: Article
• Language: English
• Subjects: Algorithms; Bias; Bias-variance tradeoff; Data Visualization; High-dimensional covariates; Humans; Inverse probability weighting; Probability Theory; Propensity Score; Treatment Outcome; Visualization
• ISSN: 1047-2797; 1873-2585
• DOI: 10.1016/j.annepidem.2019.12.006

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. 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). 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. Our results suggest that the analytic impact of all confounders should be considered carefully when fitting IPW estimators.