Pooled Prevalence
# Simulate sampling for fixed pool size and assumed known test sensitivity and specificity

### Introduction

### Input values

### Outputs

### Note

This utility simulates sampling and prevalence estimation for alternative pooling strategies for assumed values of prevalence and test sensitivity and specificity and for a specified level of confidence. The program runs multiple iterations of sampling and estimation and calculates the mean prevalence and estimated bias across all iterations. See the User Guide for more details. See demonstration analysis.

This method estimates prevalence using a method that assumes that test sensitivity and specificity are both known, and that doesn't allow for uncertainty in these estimates. However, the program also allows for alternative the estimates of the true sensitivity and specificity of the test to be entered, allowing assessment of the potential impact of inaccurate estimates of sensitivity and specificity on the resulting prevalence estimate.

Required inputs for this program are:

- assumed true prevalence of infection - between 0 and 1;
- assumed test sensitivity and specificity - between 0 and 1;
- estimates of the true test sensitivity and specificity - between 0 and 1;
- the desired level of confidence - between 0 and 1;
- the number of iterations to simulate - a positive integer; and
- the size and number of pools to be tested for each strategy to be simulated. This data should be copied and pasted from a spreadsheet format as described below.

Outputs are summarised across all iterations for each strategy entered and presented in a summary table. The main outputs are:

- mean prevalence;
- minimum and maximum prevalence estimates;
- mean bias in the estimated prevalence;
- mean (exact) confidence interval width;
- mean standard error of the estimated prevalence;
- mean squared error of the estimated prevalence (mean variance plus the square of the mean bias);
- relative bias as a proportion of the mean estimated (apparent) prevalence (AP);
- relative bias as a proportion of the specified design (true) prevalence (TP);
- squared mean bias as a proportion of the mean squared error; and
- proportion of "valid" estimates, where the confidence interval for the estimated prevalence contains the true (design) prevalence.

Data for pool sizes and associated numbers of pools tested should be pasted into the data submission area. You can enter any number of scenarios to simulate, with a new row required for each scenario. You must enter at least two columns of data, labeled "PoolSize" and "Pools". Include a header row containing the names.