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.
Input | Nilai |
---|---|
Angka diuji | 162 |
Uji angka + ve | 40 |
Sebelum prevalensi alpha | 1 |
Beta prevalensi sebelumnya | 1 |
Prior Se alpha | 4.44 |
Beta Se Sebelum | 13.31 |
Prior Sp alpha | 71.25 |
Sebelum Sp beta | 3.75 |
Iterasi | 25000 |
Buang | 5000 |
True pos start | 35 |
False neg start | 35 |
Distribusi Beta sebelumnya yang didefinisikan di atas setara dengan:
Distribusi | Nilai alfa | Nilai beta | 2.5% persentil | Median | 97.5% persentil | Berarti | Mode | Standar deviasi |
---|---|---|---|---|---|---|---|---|
Kelaziman | 1 | 1 | 0.025 | 0.5 | 0.975 | 0.5 | 0.2887 | |
Sensitivitas | 4.44 | 13.31 | 0.0843 | 0.2406 | 0.469 | 0.2501 | 0.2184 | 0.1 |
Kekhususan | 71.25 | 3.75 | 0.8909 | 0.954 | 0.9868 | 0.95 | 0.9623 | 0.025 |
Simulasi dijalankan untuk 25.000 iterasi, dengan 5.000 iterasi dibuang untuk memungkinkan konvergensi. Probabilitas posterior distribusi untuk prevalensi, sensitivitas, spesifisitas dan parameter lain dari analisis dirangkum di bawah.
Kelaziman | Sensitivitas | Kekhususan | PPV | NPV | LR untuk positif | LR untuk negatif | Benar positif | False negative | |
---|---|---|---|---|---|---|---|---|---|
Minimum | 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 |
Median | 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 |
Maksimum | 1 | 0.697 | 0.998 | 0.994 | 0.907 | 157.8 | 1 | 40 | 122 |
Berarti | 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 |
Iterasi | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 |