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.
输入 | 值 |
---|---|
经过测试 | 162 |
数字测试+ ve | 40 |
先前患病率为alpha | 1 |
先前患病率β | 1 |
之前的Se Alpha | 4.44 |
之前的Se测试版 | 13.31 |
之前的Sp alpha | 71.25 |
之前的Sp beta | 3.75 |
迭代 | 25000 |
丢弃 | 5000 |
真正的开始 | 35 |
虚假的开始 | 35 |
上面定义的先前Beta分布相当于:
分配 | 阿尔法值 | Beta值 | 2.5%百分位数 | 中位数 | 百分之97.5% | 意味着 | 模式 | 标准偏差 |
---|---|---|---|---|---|---|---|---|
流行 | 1 | 1 | 0.025 | 0.5 | 0.975 | 0.5 | 0.2887 | |
灵敏度 | 4.44 | 13.31 | 0.0843 | 0.2406 | 0.469 | 0.2501 | 0.2184 | 0.1 |
特异性 | 71.25 | 3.75 | 0.8909 | 0.954 | 0.9868 | 0.95 | 0.9623 | 0.025 |
模拟运行25,000次迭代,丢弃5,000次迭代以允许收敛。 后验概率 来自分析的患病率,敏感性,特异性和其他参数的分布总结如下.
流行 | 灵敏度 | 特异性 | PPV | NPV | LR为积极的 | LR为负面 | 真正的积极 | 假阴性 | |
---|---|---|---|---|---|---|---|---|---|
最低限度 | 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 |
中位数 | 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 |
最大 | 1 | 0.697 | 0.998 | 0.994 | 0.907 | 157.8 | 1 | 40 | 122 |
意味着 | 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 |
迭代 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 |