Quantitative Bias Analysis for a Misclassified Confounder
Epidemiology, vol. 31, pp. 796–805
Abstract
Observational data are increasingly used with the aim of estimating causal effects of treatments, through careful control for confounding. Marginal structural models estimated using inverse probability weighting (MSMs-IPW), like other methods to control for confounding, assume that confounding variables are measured without error. The average treatment effect in an MSM-IPW may however be biased when a confounding variable is error prone. Using the potential outcome framework, we derive expressions for the bias due to confounder misclassification in analyses that aim to estimate the average treatment effect using an marginal structural model estimated using inverse probability weighting (MSM-IPW). We compare this bias with the bias due to confounder misclassification in analyses based on a conditional regression model. Focus is on a point-treatment study with a continuous outcome. Compared with bias in the average treatment effect in a conditional model, the bias in an MSM-IPW can be different in magnitude but is equal in sign. Also, we use a simulation study to investigate the finite sample performance of MSM-IPW and conditional models when a confounding variable is misclassified. Simulation results indicate that confidence intervals of the treatment effect obtained from MSM-IPW are generally wider, and coverage of the true treatment effect is higher compared with a conditional model, ranging from overcoverage if there is no confounder misclassification to undercoverage when there is confounder misclassification. Further, we illustrate in a study of blood pressure-lowering therapy, how the bias expressions can be used to inform a quantitative bias analysis to study the impact of confounder misclassification, supported by an online tool.
Authors 4
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Leiden University Medical Center
Affiliation as printed
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
Data and code availability: The data and code used for the simulation study and quantitative bias analysis have been made publicly and can be accessed via. www.github.com/LindaNab/memsm
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Rolf H. H. Groenwold Aachen Department of Biomedical Data Sciences Department of Clinical Epidemiology
Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
Submitted December 11, 2019
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com)
accepted July 17, 2020
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Leiden University Medical Center
Affiliation as printed
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
Submitted December 11, 2019
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com)
accepted July 17, 2020
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London School of Hygiene & Tropical Medicine
Affiliation as printed
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
Submitted December 11, 2019
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com)
accepted July 17, 2020
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