Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models
| العنوان: | Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models |
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| المؤلفون: | Schmidt, Amand F, Klungel, Olaf H, Groenwold, Rolf H H, Sub Pharmacotherapy, Theoretical, Pharmacoepidemiology and Clinical Pharmacology |
| المصدر: | Epidemiology, 27(1), 133. Lippincott Williams and Wilkins Epidemiology, 27(1), 133. NLM (Medline) |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | Epidemiology, Marginal structural model, Logistic regression, Research Support, 01 natural sciences, Risk Assessment, EVENTS, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Statistics, Outcome Assessment, Health Care, Journal Article, INSTRUMENTAL VARIABLES, Humans, Comparative Study, Computer Simulation, CORONARY-HEART-DISEASE, 030212 general & internal medicine, 0101 mathematics, Non-U.S. Gov't, OBSERVATIONAL DATA, MARGINAL STRUCTURAL MODELS, Mathematics, Probability, Models, Statistical, Inverse probability weighting, Research Support, Non-U.S. Gov't, Confounding, RISK SCORES, Confounding Factors, Epidemiologic, Confidence interval, Regression, Nominal level, Observational Studies as Topic, PROPENSITY SCORE, Logistic Models, TRIAL GENERALIZABILITY, Research Design, Data Interpretation, Statistical, CAUSAL INFERENCE, Propensity score matching, SIMULATION |
| الوصف: | 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. |
| وصف الملف: | image/pdf; application/pdf |
| اللغة: | English |
| تدمد: | 1044-3983 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29649a042cf77eca156d0bca277f5214Test https://hdl.handle.net/1874/329750Test |
| حقوق: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....29649a042cf77eca156d0bca277f5214 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10443983 |
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