Identify the minimum number of positive test result observations needed to be confident the disease has a non-zero prevalence. — fp_signif_level • testerror

Identify the minimum number of positive test result observations needed to be confident the disease has a non-zero prevalence.

Usage

fp_signif_level(
  n_obs,
  false_pos_controls,
  n_controls,
  bonferroni = NULL,
  ...,
  spec = NULL
)

Arguments

n_obs: the number of tests performed.
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.
bonferroni: the number of simultaneous tests considered.
...: not used
spec: a prior value for specificity as a beta

Value

a vector of test positive counts which are the lowest significant value that could be regarded as not due to chance.

Examples

# lowest significant count of positives in 1000 tests 
fp_signif_level(1000, false_pos_controls = 0:5, n_controls=800)
#> [1]  0  5  7  9 11 13
fp_signif_level(c(1000,800,600,400), false_pos_controls = 1:4, n_controls=800)
#> [1] 5 6 6 5