11 - Simulate sampling for fixed pool size and assumed perfect test
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 a prefect test (using Method 2) for each iteration and calculates
the mean prevalence and estimated bias across all iterations. It assumes fixed 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%). 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 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 |
Fixed pool size and known Se and Sp |
| 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 |
Number of pools |
| 1 |
2 |
83 |
| 2 |
3 |
60 |
| 3 |
4 |
49 |
| 4 |
5 |
42 |
| 5 |
10 |
33 |
| 6 |
20 |
46 |
| 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 |
0.14101 |
0.06862 |
0.23165 |
0.00101 |
0.11593 |
0.02794 |
0.00079 |
0.00719 |
0.00724 |
0.0013 |
0.968 |
| 2 |
0.14017 |
0.06528 |
0.22434 |
0.00017 |
0.1161 |
0.02787 |
0.00079 |
0.00124 |
0.00124 |
4e-05 |
0.96 |
| 3 |
0.14037 |
0.06782 |
0.25617 |
0.00037 |
0.11666 |
0.02789 |
0.00079 |
0.0026 |
0.00261 |
0.00017 |
0.967 |
| 4 |
0.14123 |
0.06508 |
0.23506 |
0.00123 |
0.11878 |
0.02826 |
0.00081 |
0.00872 |
0.0088 |
0.00187 |
0.965 |
| 5 |
0.14534 |
0.06983 |
1 |
0.00534 |
0.12772 |
0.02923 |
0.00092 |
0.03671 |
0.03811 |
0.03097 |
0.972 |
| 6 |
0.23773 |
0.08374 |
1 |
0.09773 |
0.22332 |
0.02911 |
0.01046 |
0.41109 |
0.69804 |
0.91322 |
0.974 |
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