truePrevPools: Estimate true prevalence from pooled samples
In prevalence: Tools for Prevalence Assessment Studies

truePrevPools

R Documentation

Estimate true prevalence from pooled samples

Description

Bayesian estimation of true prevalence from apparent prevalence obtained by testing pooled samples.

Usage

truePrevPools(x, n, SE = 1, SP = 1, prior = c(1, 1),
              nchains = 2, burnin = 10000, update = 10000,
              verbose = FALSE)

Arguments

`x`	The vector of indicator variables, indicating whether a pool was positive (`"1"`) or negative (`"0"`)
`n`	The vector of pool sizes
`SE, SP`	The prior distribution for sensitivity (SE) and specificity (SP); see 'Details' below for specification of these distributions
`prior`	The parameters of the prior Beta distribution for true prevalence; defaults to `c(1, 1)`
`nchains`	The number of chains used in the estimation process; `nchains` must be ≥ 2
`burnin`	The number of discarded model iterations; defaults to 10,000
`update`	The number of withheld model iterations; defaults to 10,000
`verbose`	Logical flag, indicating if JAGS process output should be printed to the R console; defaults to `FALSE`

Details

truePrevPools calls on JAGS/rjags to estimate the true prevalence from the apparent prevalence in a Bayesian framework. The default model, in BUGS language, is given below. To see the actual fitted model, see the model slot of the prev-object.

  model {
    for (i in 1:N) {
      x[i] ~ dbern(AP[i])
      AP[i] <- SEpool[i] * (1 - pow(1 - TP, n[i])) + (1 - SPpool[i]) * pow(1 - TP, n[i])
      SEpool[i] <- 1 - (pow(1 - SE, n[i] * TP) * pow(SP, n[i] * (1 - TP)))
      SPpool[i] <- pow(SP, n[i])
    }
  # SE ~ user-defined (see below)
  # SP ~ user-defined (see below)
  TP ~ dbeta(prior[1], prior[2])
  }

The test sensitivity (SE) and specificity (SP) can be specified by the user, independently, as one of "fixed", "uniform", "beta", "pert", or "beta-expert", with "fixed" as the default. Note that SE and SP must correspond to the test characteristics for testing individual samples; truePrevPools will calculate SEpool and SPpool, the sensitivity and specificitiy for testing pooled samples, based on Boelaert et al. (2000).

Distribution parameters can be specified in a named list() as follows:

Fixed: list(dist = "fixed", par)
Uniform: list(dist = "uniform", min, max)
Beta: list(dist = "beta", alpha, beta)
PERT: list(dist = "pert", method, a, m, b, k)
'method' must be "classic" or "vose";
'a' denotes the pessimistic (minimum) estimate, 'm' the most likely estimate, and 'b' the optimistic (maximum) estimate;
'k' denotes the scale parameter.
See betaPERT for more information on Beta-PERT parametrization.
Beta-Expert: list(dist = "beta-expert", mode, mean, lower, upper, p)
'mode' denotes the most likely estimate, 'mean' the mean estimate;
'lower' denotes the lower bound, 'upper' the upper bound;
'p' denotes the confidence level of the expert.
Only mode or mean should be specified; lower and upper can be specified together or alone.
See betaExpert for more information on Beta-Expert parameterization.

For Uniform, Beta and Beta-PERT distributions, BUGS-style short-hand notation is also allowed:

Uniform: ~dunif(min, max)
Beta: ~dbeta(alpha, beta)
Beta-PERT: ~dpert(min, mode, max)

Value

An object of class prev.

Note

Markov chain Monte Carlo sampling in truePrevPools is performed by JAGS (Just Another Gibbs Sampler) through the rjags package. JAGS can be downloaded from https://mcmc-jags.sourceforge.io/.

Author(s)

Brecht Devleesschauwer <brechtdv@gmail.com>

References

Speybroeck N, Williams CJ, Lafia KB, Devleesschauwer B, Berkvens D (2012) Estimating the prevalence of infections in vector populations using pools of samples. Med Vet Entomol 26:361-371
Boelaert F, Walravens K, Biront P, Vermeersch JP, Berkvens D, Godfroid J (2000) Prevalence of paratuberculosis (Johne's disease) in the Belgian cattle population. Vet Microbiol 77:269-281

Examples

## Sandflies in Aurabani, Nepal, 2007
pool_results <- c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0)
pool_sizes <- c(2, 1, 6, 10, 1, 7, 1, 4, 1, 3)

## Sensitivity ranges uniformly between 60% and 95%
## Specificity is considered to be 100%

#> BUGS-style:
truePrevPools(x = pool_results, n = pool_sizes,
              SE = ~dunif(0.60, 0.95), SP = 1)
			  
#> list-style:
SE <- list(dist = "uniform", min = 0.60, max = 0.95)
truePrevPools(x = pool_results, n = pool_sizes,
              SE = SE, SP = 1)

prevalence documentation built on June 4, 2022, 1:05 a.m.