Misclassification of Vaccination Status in Electronic Health Records: A Bayesian Approach in Cluster Randomized Trials

Misclassification in binary outcomes is not uncommon and statistical methods to investigate its impact on policy-driving study results are lacking. While misclassifying binary outcomes is a statistically ubiquitous phenomena, we focus on misclassification in a public health application: vaccinations. One such study design in public health that addresses policy is the cluster controlled randomized trial (CCRT). A CCRT that measures the impact of a novel behavioral intervention on increasing vaccine uptake can be severely biased when the supporting data are incomplete vaccination records. In particular, these vaccine records more often may be prone to negative misclassification, that is, a clinic's record of an individual patient's vaccination status may be unvaccinated when, in reality, this patient was vaccinated outside of the clinic. With large nation-wide endeavors to encourage vaccinations without a gold-standard vaccine record system, sensitivity analyses that incorporate misclassification rates are promising for robust inference. In this work we introduce a novel extension of Bayesian logistic regression where we perturb the clinic size and vaccination count with random draws from expert-elicited prior distributions. These prior distributions represent the misclassification rates for each clinic that stochastically add unvaccinated counts to the observed vaccinated counts. These prior distributions are assigned for each clinic (the first level in a group-level randomized trial). We demonstrate this method with a data application from a CCRT evaluating the influence of a behavioral intervention on vaccination uptake among U.S. veterans. A simulation study is carried out demonstrating its estimation properties.