Lang-Reiczigel true prevalence for panel

Uses resampling to incorporate uncertainty of sensitivity and specificity into an estimate of true prevalence from a given value of apparent prevalence.

Usage

prevalence_panel_lang_reiczigel(
  panel_pos_obs,
  panel_n_obs,
  pos_obs,
  n_obs,
  false_pos_controls = NULL,
  n_controls = NULL,
  false_neg_diseased = NULL,
  n_diseased = NULL,
  ...,
  spec = spec_prior(),
  sens = sens_prior(),
  confint = 0.95,
  fmt = "%1.2f%% [%1.2f%% — %1.2f%%]",
  samples = 1000
)

Arguments

panel_pos_obs

the number of positive observations for a given panel of tests

panel_n_obs

a vector of the number of observations for each component test

pos_obs

the number of positive observations for a given test

n_obs

the number of observations for a given test

false_pos_controls

the number of positives that appeared in the specificity disease-free control group. These are by definition false positives. This is (1-specificity)*n_controls

n_controls

the number of controls in the specificity disease-free control group.

false_neg_diseased

the number of negatives that appeared in the sensitivity confirmed disease group. These are by definition false negatives. This is (1-sensitivity)*n_diseased

n_diseased

the number of confirmed disease cases in the sensitivity control group.

...

Arguments passed on to prevalence_lang_reiczigel

spec

the prior specificity of the test as a beta_dist.

sens

the prior sensitivity of the test as a beta_dist.

confint

confidence interval limits

fmt

a sprintf formatting string accepting 3 numbers

samples

number of random draws of sensitivity and specificity (optional - default 1000)

Value

the expected value of apparent prevalence

Details

This is not vectorised

Examples

#TODO