{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T19:48:16Z","timestamp":1772135296259,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,29]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>One of the main threats to the validity of causal effect estimates from observational data is the existence of unmeasured confounders. A plethora of methods has been proposed to quantify deviation from conditional exchangeability, which arises when confounding is not properly accounted for, with each method having its own set of limitations and underlying assumptions. Few methods both scale well with the increasing complexity of potential measured confounders and avoid making strong simplifying assumptions about the effect of the unmeasured confounder within strata of the measured confounders. For binary outcomes, we propose a quantification of the deviation from conditional exchangeability, based on standardization within levels of the exposure, which can accommodate any type of measured and unmeasured confounders or desired estimand. In the case of binary exposure, this amounts to varying two parameters across a grid of values, no matter how complex the measured confounding. We propose three methods of estimation for the causal estimand of interest under our proposed sensitivity analysis. This allows for an easily applied, easily interpreted sensitivity analysis that makes minimal assumptions about the type of unmeasured confounding and places no limits on the complexity of the potential measured confounders.<\/jats:p>","DOI":"10.1093\/biomet\/asac018","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T12:20:25Z","timestamp":1646828425000},"page":"1101-1116","source":"Crossref","is-referenced-by-count":3,"title":["Sensitivity analysis for unmeasured confounding in the estimation of marginal causal effects"],"prefix":"10.1093","volume":"109","author":[{"given":"I","family":"Cioc\u0103nea-Teodorescu","sequence":"first","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm 171 77, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E E","family":"Gabriel","sequence":"additional","affiliation":[{"name":"Department of Public Health, Section of Biostatistics, University of Copenhagen , Copenhagen DK-1014, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A","family":"Sj\u00f6lander","sequence":"additional","affiliation":[{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm 171 77, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"2022112917595667000_B1","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1111\/j.1541-0420.2005.00377.x","article-title":"Doubly robust estimation in missing data and causal inference models","volume":"61","author":"Bang,","year":"2005","journal-title":"Biometrics"},{"key":"2022112917595667000_B2","first-page":"395","article-title":"Assessing sensitivity to unmeasured confounding using a simulated potential confounder","volume":"9","author":"Carnegie,","year":"2016","journal-title":"J. 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