6 - Prevalencia verdadera estimada usando una prueba (no agrupada) con una muestra de Gibbs
This method uses a Bayesian approach and Gibbs sampling to estimate the true animal-level prevalence of infection based on testing of individual (not pooled) samples using a test with imperfect sensitivity and/or specificity. As for the Bayesian method for pooled sampling, the analysis requires prior estimates of true prevalence, test sensitivity and test specificity as Beta probability distributions, and outputs posterior distributions for prevalence, sensitivity and specificity. This method is preferable to the conventional (Rogan-Gladen) method for estimating true prevalence, because it allows for uncertainty about the true values for sensitivity and specificity when calculating probability limits for the true prevalence estimate, which are not routinely included in the conventional approach. It also allows incorporation of prior information on the likely true prevalence based on pre-existing estimates or expert opinion.
For this analysis, the original values for stool sampling for Strongyloides infection in Cambodian refugees from Joseph et al. (1996) were used, as listed in the table below, and 95% probability limits were calculated about the estimated prevalence.
| Entrada | Valor |
|---|---|
| Número probado | 162 |
| Prueba de número + ve | 40 |
| Previo a la prevalencia alfa | 1 |
| Previo a la prevalencia beta | 1 |
| Prior Se alpha | 4.44 |
| Prior Se beta | 13.31 |
| Prior sp alpha | 71.25 |
| Prior Sp beta | 3.75 |
| Iteraciones | 25000 |
| Descartar | 5000 |
| Inicio pos verdadero | 35 |
| False neg start | 35 |
Las distribuciones Beta anteriores definidas anteriormente son equivalentes a:
| Distribución | Valor alfa | Valor Beta | 2.5% percentil | Mediana | 97.5% percentil | Significa | Modo | Desviación estándar |
|---|---|---|---|---|---|---|---|---|
| Predominio | 1 | 1 | 0.025 | 0.5 | 0.975 | 0.5 | 0.2887 | |
| Sensibilidad | 4.44 | 13.31 | 0.0843 | 0.2406 | 0.469 | 0.2501 | 0.2184 | 0.1 |
| Especificidad | 71.25 | 3.75 | 0.8909 | 0.954 | 0.9868 | 0.95 | 0.9623 | 0.025 |
La simulación se ejecutó para 25,000 iteraciones, con 5,000 iteraciones descartadas para permitir la convergencia. Probabilidad posterior Las distribuciones para prevalencia, sensibilidad, especificidad y otros parámetros del análisis se resumen a continuación.
| Predominio | Sensibilidad | Especificidad | PPV | NPV | LR para positivo | LR para negativo | Verdaderos positivos | Falsos negativos | |
|---|---|---|---|---|---|---|---|---|---|
| Mínimo | 0.171 | 0.135 | 0.8 | 0.197 | 0.243 | 1 | 0.32 | 7 | 7 |
| 0.025 | 0.393 | 0.212 | 0.882 | 0.665 | 0.336 | 2.4 | 0.54 | 29 | 33 |
| Mediana | 0.738 | 0.307 | 0.951 | 0.883 | 0.538 | 6.4 | 0.73 | 38 | 82 |
| 0.975 | 0.985 | 0.484 | 0.986 | 0.969 | 0.786 | 24.4 | 0.85 | 40 | 120 |
| Máximo | 1 | 0.697 | 0.998 | 0.994 | 0.907 | 157.8 | 1 | 40 | 122 |
| Significa | 0.728 | 0.316 | 0.948 | 0.871 | 0.544 | 7.5 | 0.72 | 38 | 81 |
| SD | 0.165 | 0.07 | 0.027 | 0.08 | 0.124 | 6.4 | 0.08 | 3 | 25 |
| Iteraciones | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 |
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