Dataset Information

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

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.

SUBMITTER: Yu YH

PROVIDER: S-EPMC7864095 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

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

Yu Ya-Hui YH Filion Kristian B KB Bodnar Lisa M LM Brooks Maria M MM Platt Robert W RW Himes Katherine P KP Naimi Ashley I AI

Annals of epidemiology 20191213

<h4>Purpose</h4>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.<h4>Methods</h4>A SAS macro was developed for this visualizati ...[more]

PMID: 31982245

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Project description:The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9- to 11-year-old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow-up (ages 9-11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health-related research. We found that including all variables related to exposure-outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias.

Dataset Information

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

Purpose

Methods

Results

Conclusion

Publications

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

Similar Datasets

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