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13 - 模拟固定池大小的采样并以不确定的灵敏度和特异性进行测试

This program simulates sampling and prevalence estimation for a specified (design) prevalence value and level of confidence. The program runs multiple iterations of sampling, pooling and testing from an infinite population with the specified prevalence, estimates true prevalence assuming uncertain test sensitivity and specificity (using Method 5) for each iteration and calculates the mean prevalence and estimated bias across all iterations. It assumes fixed pool sizes and that the true values of both sensitivity and specificity are not known exactly and have been estimated in a limited number of samples. Values for the true sensitivity and specificity that are different to the assumed values can also be entered if desired to check the importance of the assumption of a perfect test.

For this analysis, six alternative pooling strategies were evaluated for the estimation of prevalence in a population with an assumed true prevalence of 0.14 (14%). Pool sizes and numbers of pools were previously estimated to provide 95% confidence of estimating a true prevalence of 0.14 with a precision of 0.055 (see sample size examples). This is equivalent to the observed prevalence and precision when 162 samples from little red flying foxes in Queensland were tested individually, with 22 positive results (H. Field, pers com). The sensitivity and specificity of the test were assumed to be 0.9 (90%) and 1 (100%) for prevalence estimation, and the true values were assumed to be the same as the assumed values for prevalence estimation. To allow for uncertainty about the true values of test sensitivity and specificity, it was assumed that sample sizes of 50 and 10,000, respectively, were used to estimate these values. Input values, pooling strategies and results are summarised in the tables below.

输入
方法 不确定的测试Se和Sp
假定患病率 0.14
假設靈敏度 0.9
假設的特異性 1
灵敏度的样本大小 50
特异性的样本量 10000
真正的灵敏度 0.9
真正的特异性 1
置信度 0.95
策略数量 6
迭代次数 1000
战略 泳池大小 池数
1 2 102
2 3 76
3 4 65
4 5 59
5 10 67
6 15 238
战略 平均患病率 最低患病率 最大患病率 平均偏见 平均CI宽度 平均标准误差 均方误差 偏置/AP 偏置/TP 偏置/MSE 比例有效
1 0.13979 0.06765 0.2416 -0.00021 0.10931 0.02789 0.00079 -0.00149 -0.00149 6e-05 0.956
2 0.14149 0.06785 0.25397 0.00149 0.11044 0.02817 0.00081 0.01054 0.01065 0.00276 0.954
3 0.14229 0.05578 0.24016 0.00229 0.11073 0.02825 0.00082 0.01613 0.01639 0.00644 0.961
4 0.14266 0.07427 0.26878 0.00266 0.11168 0.02849 0.00084 0.01861 0.01896 0.00841 0.958
5 0.14404 0.07286 0.27866 0.00404 0.11897 0.03035 0.00103 0.02805 0.02886 0.01584 0.978
6 0.14229 0.10554 0.37192 0.00229 0.12771 0.03258 0.00642 0.01606 0.01632 0.00081 0.998

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内容
1 固定泳池大小和完美测试
2 固定池大小和具有已知靈敏度和特異性的測試
3 固定池大小和具有不確定靈敏度和特異性的測試
4 可变池大小和完美测试
5 使用Gibbs采样器汇集流行率
6 使用Gibbs采样器进行一次测试(未计算),估计真实患病率
7 使用Gibbs采样器进行两次测试(未计算),估计真实患病率
8 固定池大小和完美测试的样本大小计算
9 固定池大小的样本大小计算和具有已知灵敏度和特异性的测试
10 固定池大小的样本大小计算和具有不确定灵敏度和特异性的测试
11 模拟固定池大小的采样并假设完美测试
12 模拟固定池大小的采样并以已知的灵敏度和特异性进行测试
13 模拟固定池大小的采样并以不确定的灵敏度和特异性进行测试
14 模拟可变池大小的采样并假设完美测试
15 使用已知靈敏度和固定池大小的測試進行混合測試來證明自由
16 估計先前Beta分佈的alpha和beta參數
17 估計指定的alpha和beta參數的Beta概率分佈