On the Effects of Misclassification in Estimating Efficacy With Application to Recent COVID-19 Vaccine Trials

The recent trials for proposed COVID-19 vaccines have garnered a considerable amount of attention and as of this writing extensive vaccination efforts are underway. The first two vaccines approved in the United States are the Moderna and Pfizer vaccines both with estimated efficacy near 95%. One question which has received limited attention, and which we address here, is what affect false positives or false negatives have on the estimated efficacy. Expressions for potential bias due to misclassification of COVID status are developed as are general formulas to adjust for misclassification, allowing for either differential or non-differential misclassification. These results are illustrated with numerical investigations pertinent to the Moderna and Pfizer trials. The general conclusion, fortunately, is that the potential misclassification of COVID status almost always would lead to underestimation of the efficacy and that correcting for false positives or negatives will typically lead to even higher estimated efficacy.