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

• Bauz-Olvera, Sergio Alex ^[2] ; Pambabay-Calero, Johny Javier ^[2] ; Nieto-Librero, Ana Belén ^[1] ; Galindo-Villardón, María Purificación ^[1]
  1. [1] Universidad de Salamanca

     Universidad de Salamanca

     Salamanca, España

     
  2. [2] Escuela Superior Politécnica del Litoral
• Localización: Proyecciones: Journal of Mathematics, ISSN 0716-0917, ISSN-e 0717-6279, Vol. 39, Nº. 5, 2020, págs. 1365-1380
• Idioma: inglés
• DOI: 10.22199/issn.0717-6279-2020-05-0083
• Enlaces
  • Texto completo
• Resumen
  • español

    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.

  • English

    Studies on the precision of diagnostic tests usually report the number of true positives, false positives, true negatives, and false negatives. There is generally a negative association between the number of true positives and true negatives, as studies that adopt less strict criteria to declare a test as positive need higher sensitivities and lower specificities. Given this particularity, the bivariate nature of the data must be preserved, by modeling sensitivity and specificity together. In this paper, we will use the bivariate hierarchical model applied to a meta-analysis data set which was an update to a previous systematic review of diagnostic tests for chronic Chagas disease. Our modeling framework was implemented with SAS NLMIXED procedure, making it possible to obtain summary measures for sensitivity and specificity, with values of 0.725 and 0.995, respectively, out of a total of 35 studies with 6057 patients. 

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