{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sensitivity-analysis-for-bias-due-to-a","title":"Sensitivity analysis for bias due to a misclassfied confounding variable in marginal structural models","arxiv_id":"1912.05800","date":"2019-12-12","proceeding":null,"authors":["Linda Nab","Rolf H. H. Groenwold","Maarten van Smeden","Ruth H. Keogh"],"abstract":"In observational research treatment effects, the average treatment effect (ATE) estimator may be biased if a confounding variable is misclassified. We discuss the impact of classification error in a dichotomous confounding variable in analyses using marginal structural models estimated using inverse probability weighting (MSMs-IPW) and compare this with its impact in conditional regression models, focusing on a point-treatment study with a continuous outcome. Expressions were derived for the bias in the ATE estimator from a MSM-IPW and conditional model by using the potential outcome framework. Based on these expressions, we propose a sensitivity analysis to investigate and quantify the bias due to classification error in a confounding variable in MSMs-IPW. Compared to bias in the ATE estimator from a conditional model, the bias in MSM-IPW can be dissimilar in magnitude but the bias will always be equal in sign. A simulation study was conducted to study the finite sample performance of MSMs-IPW and conditional models if a confounding variable is misclassified. Simulation results showed that confidence intervals of the treatment effect obtained from MSM-IPW are generally wider and coverage of the true treatment effect is higher compared to a conditional model, ranging from over coverage if there is no classification error to smaller under coverage when there is classification error. The use of the bias expressions to inform a sensitivity analysis was demonstrated in a study of blood pressure lowering therapy. It is important to consider the potential impact of classification error in a confounding variable in studies of treatment effects and a sensitivity analysis provides an opportunity to quantify the impact of such errors on causal conclusions. An online tool for sensitivity analyses was developed: https://lindanab.shinyapps.io/SensitivityAnalysis.","url_abs":"http://arxiv.org/abs/1912.05800v1","url_pdf":"http://arxiv.org/pdf/1912.05800v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"sensitivity-analysis-for-bias-due-to-a","repo_url":"https://github.com/LindaNab/memsm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}