Pooled Prevalence Calculator – Demonstration analyses

Simulate sampling for variable 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 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 & perfect test

Assumed Prevalence

0.14

Assumed Sensitivity

1

Assumed Specificity

1

True Sensitivity

1

True Specificity

1

Confidence

0.95

No. of strategies

6

No. 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 Std. Error

Mean Sq. 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 sq. error and Bias/MSE) are listed as ‘NaN’ (Not a number).

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