Evidence of the Different Associations of Prognostic Factors With Censoring Across Treatment Groups and Impact on Censoring Weight Model Specification: The Example of Anticoagulation in Atrial Fibrillation.

Abstract

Inverse probability of censoring weights (IPCWs) may reduce selection bias due to informative censoring in longitudinal studies. However, in studies with an active comparator, the associations between predictors and censoring may differ across treatment groups. We used the clinical example of anticoagulation treatment with warfarin or a direct oral anticoagulant (DOAC) in atrial fibrillation to illustrate this. The cohort of individuals initiating an oral anticoagulant during 2010-2016 was identified from the Régie de l'assurance maladie du Québec (RAMQ) databases. The parameter of interest was the hazard ratio (HR) of the composite of stroke, major bleeding, myocardial infarction, or death associated with continuous use of warfarin versus DOACs. Two strategies for the specification of the model for estimation of censoring weights were explored: exposure-unstratified and exposure-stratified. The HR associated with continuous treatment with warfarin versus DOACs adjusted with exposure-stratified IPCWs was 1.26 (95% confidence interval: 1.20, 1.33). Using exposure-unstratified IPCWs, the HR differed by 15% in favor of DOACs (1.41, 95% confidence interval: 1.34, 1.48). Not accounting for the different associations between the predictors and informative censoring across exposure groups may lead to misspecification of censoring weights and biased estimate on comparative effectiveness and safety.