prevalence (version 0.4.1)

truePrev: Estimate true prevalence from individuals samples

Description

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

Usage

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

Arguments

x

The apparent number of positive samples

n

The sample size

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; 'n' must be \(\ge 2\)

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

Value

An object of class prev.

Details

truePrev 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 {
    x ~ dbin(AP, n)
    AP <- SE * TP + (1 - SP) * (1 - TP)
    # 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, independently, as one of "fixed", "uniform", "beta", "pert", or "beta-expert", with "fixed" as the default.

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)

• 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 parametrization.

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)

References

• Speybroeck N, Devleesschauwer B, Joseph L, Berkvens D (2013) Misclassification errors in prevalence estimation: Bayesian handling with care. Int J Public Health 58:791-795

• Online Shiny application: https://cbra.shinyapps.io/truePrev/

See Also

coda for various functions that can be applied to the prev@mcmc object truePrevMulti: estimate true prevalence from apparent prevalence obtained by testing individual samples with multiple tests, using a conditional probability scheme truePrevMulti2: estimate true prevalence from apparent prevalence obtained by testing individual samples with multiple tests, using a covariance scheme truePrevPools: estimate true prevalence from apparent prevalence obtained by testing pooled samples betaPERT: calculate the parameters of a Beta-PERT distribution betaExpert: calculate the parameters of a Beta distribution based on expert opinion

Examples

# NOT RUN {
## Taenia solium cysticercosis in Nepal
## 142 positives out of 742 pigs sampled

## Model SE and SP based on literature data
## Sensitivity ranges uniformly between 60% and 100%
## Specificity ranges uniformly between 75% and 100%
#> BUGS-style:
truePrev(x = 142, n = 742,
         SE = ~dunif(0.60, 1.00), SP = ~dunif(0.75, 1.00))

#> list-style:
SE <- list(dist = "uniform", min = 0.60, max = 1.00)
SP <- list(dist = "uniform", min = 0.75, max = 1.00)
truePrev(x = 142, n = 742, SE = SE, SP = SP)

## Model SE and SP based on expert opinions
## Sensitivity lies in between 60% and 100%; most likely value is 90%
## Specificity is with 95% confidence larger than 75%; most likely value is 90%
SE <- list(dist = "pert", a = 0.60, m = 0.90, b = 1.00)
SP <- list(dist = "beta-expert", mode = 0.90, lower = 0.75, p = 0.95)
truePrev(x = 142, n = 742, SE = SE, SP = SP)

## Model SE and SP as fixed values (each 90%)
truePrev(x = 142, n = 742, SE = 0.90, SP = 0.90)
# }