# User guide

## Important Assumptions

As with conventional methods for estimating prevalence, pooled testing methods and bayesian methods for estimating prevalence depend on a number of important assumptions. Violation of these assumptions will result in biased estimates. Key assumptions are that:

- the outcome is assumed to follow a binomial distribution - clustering or overdispersion of the positive outcome can cause substantial bias in the resulting estimate,
- the health status of each individual is independent of the status of others, both within and between pools,
- sampling of the population is by simple random sampling,
- individual samples are allocated into groups (pools) by random selection,
- sample size is small relative to the population being sampled,
- assumed values for sensitivity and specificity are appropriate,
- dilution of individual samples by pooling has no effect on sensitivity or specificity estimates (or the estimates used take any dilution effects into account),
- samples are assumed to be mixed homogeneously in the pools and any sub-samples taken for testing are equally representative of all of the individuals contributing to each pool,
- all pools represent the same number of individuals (except for the variable pool-size method),
- assumed prior distributions for prevalence, sensitivity and specificity for Bayesian methods are appropriate and
- assumed true prevalence for sample size calculations and simulations is appropriate.