Adjusting for Confounding in Early Postlaunch Settings: Going beyond Logistic Regression Models

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Adjusting for Confounding in Early Postlaunch Settings: Going beyond Logistic Regression Models

Schmidt, AF; Klungel, OH; Groenwold, RHH; (2016) Adjusting for Confounding in Early Postlaunch Settings: Going beyond Logistic Regression Models. Epidemiology , 27 (1) pp. 133-142. 10.1097/EDE.0000000000000388. Green open access

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Abstract

Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. Background: Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. Methods: We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. Results: At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than-100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of-8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. Conclusion: In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

Type:	Article
Title:	Adjusting for Confounding in Early Postlaunch Settings: Going beyond Logistic Regression Models
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1097/EDE.0000000000000388
Publisher version:	http://dx.doi.org/10.1097/EDE.0000000000000388
Language:	English
Additional information:	Copyright © 2016 Wolters Kluwer Health, Inc. This article is published under a Creative Commons Attribution Non-commercial Non-derivative 4.0 International license (CC BY-NC-ND 4.0). This license allows you to share, copy, distribute and transmit the work for personal and non-commercial use providing author and publisher attribution is clearly stated. Further details about CC BY licenses are available at http://creativecommons.org/ licenses/by/4.0. Access may be initially restricted by the publisher.
UCL classification:	UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine
URI:	https://discovery.ucl.ac.uk/id/eprint/1471911