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.

Input | Value |
---|---|

Method | Uncertain test Se and Sp |

Assumed Prevalence | 0.14 |

Assumed Sensitivity | 0.9 |

Assumed Specificity | 1 |

Sample size for sensitivity | 50 |

Sample size for specificity | 10000 |

True Sensitivity | 0.9 |

True Specificity | 1 |

Confidence | 0.95 |

Number of strategies | 6 |

Number of iterations | 1000 |

Strategy | Pool size | Number of pools |
---|---|---|

1 | 2 | 102 |

2 | 3 | 76 |

3 | 4 | 65 |

4 | 5 | 59 |

5 | 10 | 67 |

6 | 15 | 238 |

Strategy | Mean prevalence | Minimum prevalence | Maximum prevalence | Mean bias | Mean CI width | Mean standard error | Mean square error | Bias/AP | Bias/TP | Bias/MSE | Proportion valid |
---|---|---|---|---|---|---|---|---|---|---|---|

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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