View source: R/truePrevMulti-main.R
truePrevMulti | R Documentation |
Bayesian estimation of true prevalence from apparent prevalence obtained by applying multiple tests to individual samples. truePrevMulti
implements the approach described by Berkvens et al. (2006), which uses a multinomial distribution to model observed test results, and in which conditional dependence between tests is modelled through conditional probabilities.
truePrevMulti(x, n, prior, nchains = 2, burnin = 10000, update = 10000, verbose = FALSE)
x |
Vector of apparent test results; see 'Details' below |
n |
The total sample size |
prior |
The prior distribution for |
nchains |
The number of chains used in the estimation process; 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 |
truePrevMulti
calls on JAGS via the rjags package to estimate true prevalence from apparent prevalence in a Bayesian framework. truePrevMulti
fits a multinomial model to the apparent test results obtained by testing individual samples with a given number of tests. To see the actual fitted model, see the model slot of the prev
-object.
The vector of apparent tests results, x
, must contain the number of samples corresponding to each combination of test results. To see how this vector is defined for the number of tests h
at hand, use define_x
.
The prior in the multinomial model consists of a vector theta
, which holds values for the true prevalence (TP), the sensitivity and specificity of the first test (SE1, SP1), and the conditional dependencies between the results of the subsequent tests and the preceding one(s). To see how this vector is defined for the number of tests n
at hand, use define_prior
.
The values of prior
can be specified in two ways, referred to as BUGS-style and list-style, respectively. See also below for some examples.
For BUGS-style specification, the values of theta
should be given between curly brackets (i.e., {}
), separated by line breaks. theta
values can be specified to be deterministic (i.e., fixed), using the <-
operator, or stochastic, using the ~
operator. In the latter case, the following distributions can be used:
Uniform: dunif(min, max)
Beta: dbeta(alpha, beta)
Beta-PERT: dpert(min, mode, max)
Alternatively, theta
values 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 parameterization.
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.
An object of class prev
.
Markov chain Monte Carlo sampling in truePrevMulti
is performed by JAGS (Just Another Gibbs Sampler) through the rjags package. JAGS can be downloaded from https://mcmc-jags.sourceforge.io/.
Brecht Devleesschauwer <brechtdv@gmail.com>
Berkvens D, Speybroeck N, Praet N, Adel A, Lesaffre E (2006) Estimating disease prevalence in a Bayesian framework using probabilistic constraints. Epidemiology 17:145-153
Habib I, Sampers I, Uyttendaele M, De Zutter L, Berkvens D (2008) A Bayesian modelling framework to estimate Campylobacter prevalence and culture methods sensitivity: application to a chicken meat survey in Belgium. J Appl Microbiol 105:2002-2008
Geurden T, Berkvens D, Casaert S, Vercruysse J, Claerebout E (2008) A Bayesian evaluation of three diagnostic assays for the detection of Giardia duodenalis in symptomatic and asymptomatic dogs. Vet Parasitol 157:14-20
define_x
: how to define the vector of apparent test results x
define_prior
: how to define the vector of theta
values in prior
coda for various functions that can be applied to the prev@mcmc
object
truePrevMulti2
: estimate true prevalence from apparent prevalence obtained by testing individual samples with multiple tests, using a covariance scheme
truePrev
: estimate true prevalence from apparent prevalence obtained by testing individual samples with a single test
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
## Not run: ## ===================================================== ## ## 2-TEST EXAMPLE: Campylobacter ## ## ----------------------------------------------------- ## ## Two tests were performed on 656 chicken meat samples ## ## -> T1 = enrichment culture ## ## -> T2 = direct plating ## ## The following assumption were made: ## ## -> TP is larger than 45% and smaller than 80% ## ## -> SE1 must lie within 24% and 50% ## ## -> SP1 and SP2 both equal 100% ## ## -> beta(30, 12) describes P(T2+|D+,T1+) ## ## The following results were obtained: ## ## -> 113 samples T1+,T2+ ## ## -> 46 samples T1+,T2- ## ## -> 156 samples T1-,T2+ ## ## -> 341 samples T1-,T2- ## ## ===================================================== ## ## how is the 2-test model defined? define_x(2) define_prior(2) ## fit campylobacter 2-test model campy <- truePrevMulti( x = c(113, 46, 156, 341), n = 656, prior = { theta[1] ~ dunif(0.45, 0.80) theta[2] ~ dunif(0.24, 0.50) theta[3] <- 1 theta[4] ~ dbeta(30, 12) theta[5] ~ dbeta(1, 1) theta[6] <- 1 theta[7] <- 1 } ) ## fit same model using 'list-style' campy <- truePrevMulti( x = c(113, 46, 156, 341), n = 656, prior = list( theta1 = list(dist = "uniform", min = 0.45, max = 0.80), theta2 = list(dist = "uniform", min = 0.24, max = 0.50), theta3 = 1, theta4 = list(dist = "beta", alpha = 30, beta = 12), theta5 = list(dist = "beta", alpha = 1, beta = 1), theta6 = 1, theta7 = 1 ) ) ## show model results campy ## explore model structure str(campy) # overall structure str(campy@par) # structure of slot 'par' str(campy@mcmc) # structure of slot 'mcmc' campy@model # fitted model campy@diagnostics # DIC, BGR and Bayes-P values ## standard methods print(campy) summary(campy) par(mfrow = c(2, 2)) plot(campy) # shows plots of TP by default plot(campy, "SE1") # same plots for SE1 plot(campy, "SE2") # same plots for SE2 ## coda plots of TP, SE1, SE2 par(mfrow = c(1, 3)) densplot(campy, col = "red") traceplot(campy) gelman.plot(campy) autocorr.plot(campy) ## ===================================================== ## ## 3-TEST EXAMPLE: Giardia ## ## ----------------------------------------------------- ## ## Three tests were performed on stools from 272 dogs ## ## -> T1 = immunofluorescence assay ## ## -> T2 = direct microscopy ## ## -> T3 = SNAP immunochromatography ## ## The following assumption were made: ## ## -> TP is smaller than 20% ## ## -> SE1 must be higher than 80% ## ## -> SP1 must be higher than 90% ## ## The following results were obtained: ## ## -> 6 samples T1+,T2+,T3+ ## ## -> 4 samples T1+,T2+,T3- ## ## -> 12 samples T1+,T2-,T3+ ## ## -> 12 samples T1+,T2-,T3- ## ## -> 1 sample T1-,T2+,T3+ ## ## -> 14 samples T1-,T2+,T3- ## ## -> 3 samples T1-,T2-,T3+ ## ## -> 220 samples T1-,T2-,T3- ## ## ===================================================== ## ## how is the 3-test model defined? define_x(3) define_prior(3) ## fit giardia 3-test model giardia <- truePrevMulti( x = c(6, 4, 12, 12, 1, 14, 3, 220), n = 272, prior = { theta[1] ~ dunif(0.00, 0.20) theta[2] ~ dunif(0.90, 1.00) theta[3] ~ dunif(0.80, 1.00) theta[4] ~ dbeta(1, 1) theta[5] ~ dbeta(1, 1) theta[6] ~ dbeta(1, 1) theta[7] ~ dbeta(1, 1) theta[8] ~ dbeta(1, 1) theta[9] ~ dbeta(1, 1) theta[10] ~ dbeta(1, 1) theta[11] ~ dbeta(1, 1) theta[12] ~ dbeta(1, 1) theta[13] ~ dbeta(1, 1) theta[14] ~ dbeta(1, 1) theta[15] ~ dbeta(1, 1) } ) ## show model results giardia ## coda densplots par(mfcol = c(2, 4)) densplot(giardia, col = "red") ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.