On the Effects of Misclassification in Estimating Efficacy With Application to Recent COVID-19 Vaccine Trials
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Abstract
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.
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SciScore for 10.1101/2020.12.04.20244244: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar …
SciScore for 10.1101/2020.12.04.20244244: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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