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.

Preview	Text fig1PAPER.pdf Download (9kB) | Preview
Preview	Text fig2PAPER.pdf Download (4kB) | Preview
Preview	Text fig3PAPER.pdf Download (5kB) | Preview
Preview	Text AFSCHMIDT_title_and_body_combined.pdf Download (848kB) | Preview
Preview	Text AFSCHMIDT Table1.pdf Download (210kB) | Preview
Preview	Text AFSCHMIDT Table2.pdf Download (104kB) | Preview
Preview	Text AFSCHMIDT Table3.pdf Download (89kB) | Preview
Preview	Text AFSCHMIDT OnlineSupplement EPI.pdf Download (151kB) | Preview

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.

Download activity - last month

Export as JSONCSVXML

Download activity - last 12 months

Export as XMLCSVJSON

Downloads by country - last 12 months

Export as JSONCSVXML

Archive Staff Only

View Item