Bayesian models true prevalence for panel — bayesian_panel_complex

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

Usage

bayesian_panel_complex_model(
  test_results = testerror::.input_data,
  false_pos_controls = NULL,
  n_controls = NULL,
  false_neg_diseased = NULL,
  n_diseased = NULL,
  ...,
  sens = uniform_prior(),
  spec = uniform_prior(),
  panel_sens = uniform_prior(),
  panel_spec = uniform_prior(),
  panel_name = "Panel",
  confint = 0.95,
  fmt = "%1.2f%% [%1.2f%% — %1.2f%%]",
  chains = 4,
  warmup = 1000,
  iter = 2000,
  cache_result = TRUE
)

Arguments

test_results

A dataframe containing the following columns:

id (character) - the patient identifier
test (factor) - the test type
result (logical) - the test result

Ungrouped.

No default value.

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.

...

not used

sens

the prior sensitivity of the test as a beta_dist.

spec

the prior specificity of the test as a beta_dist.

panel_sens

the prior sensitivity of the panel as a beta_dist (optional)

panel_spec

the prior specificity of the panel as a beta_dist (optional)

panel_name

the name of the panel for combined result

confint

confidence interval limits

fmt

a sprintf formatting string accepting 3 numbers

chains

A positive integer specifying the number of Markov chains. The default is 4.

warmup

A positive integer specifying the number of warmup (aka burnin) iterations per chain. If step-size adaptation is on (which it is by default), this also controls the number of iterations for which adaptation is run (and hence these warmup samples should not be used for inference). The number of warmup iterations should be smaller than iter and the default is iter/2.

iter

A positive integer specifying the number of iterations for each chain (including warmup). The default is 2000.

cache_result

save the result of the sampling in memory for the current session

Value

a list of dataframes containing the prevalence, sensitivity, and specificity estimates, and a stanfit object with the raw fit data

Details

This is not vectorised