Optimal classification and generalized prevalence estimates for diagnostic settings with more than two classes.
Math Biosci
; 358: 108982, 2023 04.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2242906
ABSTRACT
An accurate multiclass classification strategy is crucial to interpreting antibody tests. However, traditional methods based on confidence intervals or receiver operating characteristics lack clear extensions to settings with more than two classes. We address this problem by developing a multiclass classification based on probabilistic modeling and optimal decision theory that minimizes the convex combination of false classification rates. The classification process is challenging when the relative fraction of the population in each class, or generalized prevalence, is unknown. Thus, we also develop a method for estimating the generalized prevalence of test data that is independent of classification of the test data. We validate our approach on serological data with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) naïve, previously infected, and vaccinated classes. Synthetic data are used to demonstrate that (i) prevalence estimates are unbiased and converge to true values and (ii) our procedure applies to arbitrary measurement dimensions. In contrast to the binary problem, the multiclass setting offers wide-reaching utility as the most general framework and provides new insight into prevalence estimation best practices.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
SARS-CoV-2
/
COVID-19
Tipo de estudio:
Estudios diagnósticos
/
Estudio observacional
/
Estudio pronóstico
Tópicos:
Vacunas
Límite:
Humanos
Idioma:
Inglés
Revista:
Math Biosci
Año:
2023
Tipo del documento:
Artículo
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