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 for each iteration and calculates the mean prevalence and estimated bias across all iterations. It assumes variable pool sizes and a test with 100% sensitivity and specificity. Values for the true sensitivity and specificity that are different to the assumed values of 100% 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%). 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. Pool sizes and numbers of pools were used to provide the same total sample size (210 samples) as used for the fixed pool size and perfect test example. The true sensitivity and specificity of the test were both assumed to be 1 (100%), equal to the assumed values for prevalence estimation. Input values, pooling strategies and results are summarised in the tables below.

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

Method | Variable pool size and perfect test |

Assumed Prevalence | 0.14 |

Assumed Sensitivity | 1 |

Assumed Specificity | 1 |

True Sensitivity | 1 |

True Specificity | 1 |

Confidence | 0.95 |

Number of strategies | 6 |

Number of iterations | 1000 |

Strategy | Pool size 1 | Number of pools 1 | Pool size 2 | Number of pools 2 |
---|---|---|---|---|

1 | 5 | 42 | 0 | 0 |

2 | 5 | 40 | 1 | 10 |

3 | 5 | 40 | 10 | 1 |

4 | 10 | 21 | 0 | 0 |

5 | 10 | 20 | 1 | 10 |

6 | 20 | 10 | 10 | 1 |

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 | 0.14272 | 0.05293 | 0.28226 | 0.00272 | 0.11136 | NaN | NaN | 0.01909 | 0.01946 | NaN | 0.945 |

2 | 0.14256 | 0.05826 | 0.27087 | 0.00256 | 0.11016 | NaN | NaN | 0.01793 | 0.01826 | NaN | 0.935 |

3 | 0.14161 | 0.05986 | 0.25929 | 0.00161 | 0.11187 | NaN | NaN | 0.01139 | 0.01153 | NaN | 0.97 |

4 | 0.15136 | 0.06262 | 0.92587 | 0.01136 | 0.14941 | NaN | NaN | 0.07507 | 0.08116 | NaN | 0.935 |

5 | 0.14654 | 0.06912 | 0.32703 | 0.00654 | 0.1433 | NaN | NaN | 0.0446 | 0.04669 | NaN | 0.949 |

6 | 0.49031 | 0.04148 | 0.89974 | 0.35031 | 0.18629 | NaN | NaN | 0.71447 | 2.50225 | NaN | 0.49 |

The standard error of the estimate cannot be calculated using this method, so that and other measures derived from it (Mean square error and Bias/MSE) are listed as NaN (not a number).

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