Ya-Hui Yu1, Kristian B Filion2, Lisa M Bodnar3, Maria M Brooks4, Robert W Platt5, Katherine P Himes6, Ashley I Naimi7. 1. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal. 2. 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. 3. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA; Department of Obstetrics, Gynecology, and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA; Magee-Womens Research Institute, Pittsburgh, PA. 4. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA. 5. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal; McGill University Health Center Research Institute, Montreal, QC, Canada; Department of Pediatrics, McGill University, Montreal, QC, Canada. 6. Department of Obstetrics, Gynecology, and Reproductive Sciences, School of Medicine, University of Pittsburgh, Pittsburgh, PA; Magee-Womens Research Institute, Pittsburgh, PA. 7. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA. Electronic address: ashley.naimi@pitt.edu.
Abstract
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.
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.
Authors: Jessica M Franklin; Sebastian Schneeweiss; Jennifer M Polinski; Jeremy A Rassen Journal: Comput Stat Data Anal Date: 2014-04 Impact factor: 1.681