Por favor, use este identificador para citar o enlazar este ítem:
http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/6990| On the Effects of Misclassification in Estimating Efficacy With Application to Recent COVID-19 Vaccine Trials | |
| John Buonaccorsi | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2020.12.04.20244244 | |
| Understandably, the recent trials for COVID-19 vaccines have garnered a considerable amount of attention and (as of this writing) vaccinations are about to begin. The popular summaries give infection rates in the vaccinated and placebo and estimated efficacy, which for the two trials we focus on (Moderna and Pfizer) are both near 95\%. This paper explores the potential effects of possible false positives or false negatives (misclassification) in the COVID-19 diagnosis with specific application to the Moderna and Pfizer trials. The general conclusion, fortunately, is that these potential misclassifications almost always would lead to underestimation of the efficacy and that correcting for false positives or negatives will lead to even higher estimated efficacy. | |
| medRxiv and bioRxiv | |
| 04-12-2020 | |
| Preimpreso | |
| www.medrxiv.org | |
| Inglés | |
| Epidemia COVID-19 | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos científicos |
Cargar archivos:
| Fichero | Tamaño | Formato | |
|---|---|---|---|
| On the effects of misclassification in estimating.pdf | 233.01 kB | Adobe PDF | Visualizar/Abrir |