title: Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.
abstract: 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%.
  
  
  CONCLUSIONS
  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.
authors.affiliation: From the aJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands; bDivision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht, The Netherlands; cDepartment of Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands; and dInstitute of Cardiovascular Science, Faculty of Population Health, University College London, London, United Kingdom.
authors.forename: Amand F
authors.initials: AF
authors.lastname: Schmidt
authors.forename: Olaf H
authors.initials: OH
authors.lastname: Klungel
authors.forename: Rolf H H
authors.initials: RH
authors.lastname: Groenwold
mesh_terms: D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D064887:Observational Studies as Topic / Q000379:methods; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011336:Probability; D057216:Propensity Score; D012107:Research Design; D018570:Risk Assessment