8 - Prevalencia agrupada utilizando una muestra de Gibbs

Este método usa unBayesiano enfoque y un Muestra de Gibbs iterative model to estimate the posterior distribution of the true animal-level prevalence of infection for a given pool size, number of pools tested, number of pools positive and estimated test sensitivity and specificity. See Cowling et al. (1999) (Método 7) o Mendoza-Blanco et al. (1996) para más detalles.

Las entradas requeridas para este método son:

• tamaño de grupo;
• número de piscinas probadas;
• número de agrupaciones positivas;
• parámetros alfa y beta para prior Distribuciones Beta para:
  • supuesto verdaderoprevalencia;
  • prueba estimadasensibilidad; y
  • prueba estimadaespecificidad.
• el número deiteraciones para ser simulado en la muestra de Gibbs;
• el número de iteraciones a serdescartado para permitir la convergencia del modelo; y
• inferior y upper probability (confidence) limits for summarising the output distributions.

Pool size, number of pools and number of pools positive must be positive integers and the number of positive pools must be less than the number of pools tested. Alpha and beta parameters for prevalence, sensitivity and specificity must be >0 and upper and lower confidence limits must be >0 and <1. The number of iterations and the number discarded must both be positive integers (>0) and the number discarded must be less than the number of iterations.

ElMuestra de Gibbs se utiliza para estimar el distribuciones de probabilidad posteriores of true prevalence, sensitivity and specificity that best fit the data and the prior distributions provided.

Prior estimates of the true prevalence and test sensitivity and specificity may be based on expert knowledge or on previous data. These estimates are specified as Distribuciones de probabilidad Beta, con parámetrosalfa y beta. Beta probability distributions are commonly used to express uncertainty about a proportion based on a random sample of individuals. In this situation, if x individuals are positive for a characteristic out of n examined, then the alpha and beta parameters can be calculated as alpha = x + 1 and beta = n - x + 1. Alternatively, alpha and beta can be calculated using the Utilidad de distribución Beta, provided estimates of the mode and 5% or 95% confidence limits are available from expert opinion.

Las salidas del muestreador de Gibbs sondistribuciones de probabilidad posteriores para prevalencia a nivel animal, sensibilidad de prueba y especificidad de prueba, descritas por su:

• mínimo;
• límite inferior de probabilidad;
• mediana;
• límite superior de probabilidad;
• máximo;
• significa;
• desviación estándar;
• gráfico de histograma y densidad (descargue haciendo clic en el ícono correspondiente); y
• text files of parameter estimates for all iterations of the Gibbs sampler (download by clicking on the appropriate icon).

Because the Gibbs sampler estimates prevalence iteratively, based on the data and the prior distributions, it may take a number of iteraciones for the model to converge on the true value. Therefore, a specified number of initial iterations must be descartado (not used for estimation) to allow the model to converge on the true values. This number must be sufficient to allow convergence, and should be at least 2000 - 5000. It is also important to carry out an adequate number of iterations to support inference from the results. Suggested minimum values for the total number of iterations and the number to be discarded are provided, but can be varied if desired.

Este análisis puede tardar varios minutos en completarse, dependiendo del número de iteraciones requeridas.