Randomized in error in pragmatic clinical trials

Pragmatic trials that combine electronic health record data and patient-reported data may be subject to selection bias due to the differential post-randomization exclusion of participants who are randomized in error. Such situations are often caused by inevitable reasons, such as incomplete patient medical records at the pre-randomization stage. This can lead to participants in the intervention arm being identified as ineligible after randomization, while randomized-in-error participants in the usual care are often not discernable. The differential exclusion can present analytic challenges and threaten result validity.

Under the potential outcomes framework, we developed a Bayesian model that jointly identifies the randomized-in-error status and estimates the average treatment effect among participants not randomized in error. We designed simulation studies with hypothesized proportions of 5 %-15 % randomization in error to evaluate the performance of our model across scenarios where the outcomes of participants randomized in error were either measured or unmeasured. Comparisons were made to intention-to-treat and covariate-adjusted estimators.

Simulation results show satisfactory performance of our proposed models, where the estimated average treatment effects among participants not randomized in error have low bias (<1 %) and close to 95 % coverage. Estimates from the alternative approaches can exhibit notable biases and low coverage.

Differential exclusion in pragmatic clinical trials after randomization can lead to selection bias. Under certain assumptions, Bayesian methods provide a feasible solution to jointly identify randomized-in-error status and estimate the average treatment effect among participants not randomized in error, ensuring more reliable and valid inferences about intervention effects.