True prevalence from apparent prevalence with uncertainty — uncertain_rogan

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

Usage

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

Arguments

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.
...: not used
spec: the prior specificity of the test as a beta_dist.
sens: the prior sensitivity of the test as a beta_dist.
samples: number fo random draws of sensitivity and specificity
confint: confidence interval limits
fmt: a sprintf formatting string accepting 3 numbers
seed: set seed for reproducibility

Value

the expected value of apparent prevalence

Examples

uncertain_rogan_gladen(
  pos_obs = 20, n_obs = 1000, 
  false_pos_controls = 10, n_controls = 800, 
  false_neg_diseased = 20, n_diseased = 100)
#> # A tibble: 1 × 13
#>   prevalence.median prevalence.lower prevalence.upper prevalence.label     
#>               <dbl>            <dbl>            <dbl> <chr>                
#> 1            0.0101                0           0.0180 1.01% [0.00% — 1.80%]
#> # ℹ 9 more variables: spec.median <dbl>, spec.lower <dbl>, spec.upper <dbl>,
#> #   spec.label <chr>, sens.median <dbl>, sens.lower <dbl>, sens.upper <dbl>,
#> #   sens.label <chr>, prevalence.method <chr>
  
uncertain_rogan_gladen(
  pos_obs = 5, n_obs = 1000, 
  sens = beta_dist(p=0.75,n=200), 
  spec = beta_dist(p=0.9975, n=800))
#> # A tibble: 1 × 13
#>   prevalence.median prevalence.lower prevalence.upper prevalence.label     
#>               <dbl>            <dbl>            <dbl> <chr>                
#> 1           0.00394                0          0.00625 0.39% [0.00% — 0.62%]
#> # ℹ 9 more variables: spec.median <dbl>, spec.lower <dbl>, spec.upper <dbl>,
#> #   spec.label <chr>, sens.median <dbl>, sens.lower <dbl>, sens.upper <dbl>,
#> #   sens.label <chr>, prevalence.method <chr>

uncertain_rogan_gladen(
  pos_obs = c(5,10), n_obs = c(1000,1000), 
  false_pos_controls = c(2,1), n_controls = c(800,800), 
  false_neg_diseased = c(25,20),n_diseased = c(100,100))
#> # A tibble: 2 × 13
#>   prevalence.median prevalence.lower prevalence.upper prevalence.label     
#>               <dbl>            <dbl>            <dbl> <chr>                
#> 1           0.00382          0                0.00658 0.38% [0.00% — 0.66%]
#> 2           0.0113           0.00694          0.0132  1.13% [0.69% — 1.32%]
#> # ℹ 9 more variables: spec.median <dbl>, spec.lower <dbl>, spec.upper <dbl>,
#> #   spec.label <chr>, sens.median <dbl>, sens.lower <dbl>, sens.upper <dbl>,
#> #   sens.label <chr>, prevalence.method <chr>