Bivariate hierarchical model for the Meta-analysis of diagnostic tests in studies with binary responses: its application from SAS and R

  1. Bauz-Olvera, Sergio Alex 1
  2. Pambabay-Calero, Johny Javier 1
  3. Nieto-Librero, Ana Belén 2
  4. Galindo-Villardón, María Purificación 2
  1. 1 Escuela Superior Politecnica del Litoral
    info

    Escuela Superior Politecnica del Litoral

    Guayaquil, Ecuador

    ROR https://ror.org/04qenc566

  2. 2 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Revista:
Proyecciones: Journal of Mathematics

ISSN: 0716-0917,  0717-6279

Año de publicación: 2020

Volumen: 39

Número: 5

Páginas: 1365-1380

Tipo: Artículo

DOI: 10.22199/ISSN.0717-6279-2020-05-0083 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Proyecciones: Journal of Mathematics

Resumen

Los estudios de precisión de las pruebas diagnósticas normalmente informan el número de verdaderos positivos, falsos positivos, verdaderos negativos y falsos negativos. Por lo general, existe una asociación negativa entre el número de verdaderos positivos y verdaderos negativos, porque los estudios que adoptan un criterio menos estricto para declarar un test positivo necesitan sensibilidades más altas y especificidades más bajas. Debido a esta particularidad, se debe conservar la naturaleza bivariante de los datos, modelizando la sensibilidad y especificidad de manera conjunta. En este trabajo, se empleará el modelo jerárquico bivariante aplicado a un conjunto de datos de un meta-análisis que fue una actualización de una revisión sistemática previa sobre pruebas de diagnóstico para la enfermedad de Chagas crónica. Nuestro marco de modelización se implementa con el procedimiento NLMIXED de SAS, permitiendo obtener medidas resúmenes para la sensibilidad y especificidad con valores de 0.725 y 0.995 respectivamente, de un total de 35 estudios que involucraron 6057 pacientes.

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Fuente de los datos: Dialnet