Background: Observational healthcare data can be used for drug safety and effectiveness research. The use of inverse probability of treatment weights (IPW) reduces measured confounding under the assumption of accurate measurement of the outcome variable; however, many datasets suffer from systematic outcome misclassification.
Methods: We introduced a modification to IPW to correct for the presence of outcome misclassification. To demonstrate the utility of these modified weights in realistic settings, we investigated postmyocardial infarction statin use and the 1-year risk of stroke in the Clinical Practice Research Datalink.
Results: We computed an IPW-adjusted odds ratio (OR = 0.67; 95% confidence interval (CI) = 0.48, 0.93). We employed a technique to modify IPW for the presence of outcome misclassification using linked hospital records for outcome validation (modified IPW adjusted OR = 0.77; 95% CI = 0.52, 1.15) and compared the results with a meta-analysis of randomized controlled trials (RCTs) (pooled OR = 0.80; 95% CI = 0.74, 0.87). Finally, we present simulation studies to investigate the impact of model selection on bias reduction and variability.
Conclusion: Ignoring outcome misclassification yielded biased estimates whereas the use of the modified IPW approach produced encouraging results when compared with the meta-analytic RCT findings.