Pooled Prevalence

Simulate sampling for variable pool sizes

Design parameters:

Assumed true prevalence

True test sensitivity

True test specificity

Desired confidence level

0.9 0.95 0.98 0.99

Simulation details:

Number of iterations

1,000 2,000 5,000 10,000

Paste data in the space below. Data columns can be in any order but must include columns labeled "Strategy", "Poolsize" and "Pools". A header row specifying column names must also be included.

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Introduction

This utility simulates sampling and prevalence estimation for up to 6 different pooling strategies for and up to 5 different pool sizes for each strategy. Simulations assume perfect test sensitivity and specificity and the specified assumed prevalence in the population. The program runs multiple iterations of sampling and estimation and calculates the mean prevalence and confidence limits for the specified level of confidence across all iterations and estimates the level of bias in the prevalence estimates. See the User Guide for more details. See demonstration analysis.

The program uses a generalised linear model to estimate prevalence and confidence limits for variable pool sizes and compares the mean estimates to the assumed (design) prtevalence to estimate bias. The program assumes that the test used has perfect sensitivity and specificity but allows for the estimates of sensitivity and specificity to be incorrect, allowing assessment of the potential impact of inaccurate estimates on the resulting prevalence estimate.

Input values

Required inputs for this program are:

• assumed true prevalence of infection - between 0 and 1;
• 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

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

• mean prevalence;
• mean bias in the estimated prevalence;
• mean confidence interval width;
• relative bias as a proportion of the mean estimated (apparent) prevalence (AP);
• relative bias as a proportion of the specified design (true) prevalence (TP); and
• proportion of "valid" estimates, where the confidence interval for the estimated prevalence contains the true (design) prevalence.

Note

Data for pool sizes and associated numbers of pools tested should be pasted into the data submission area. You can enter any number of strategies to simulate, with a new row required for each strategy. You must enter at least three columns of data, labeled "Strategy", "PoolSize" and "Pools". Where there are multiple pool sizes for a strategy enter each pool size and corresponding number of pools on a new row. Include a header row containing the names.