Bayesian serological sampling functions for one population and unknown test performance

one population - unknown test performance - posterior distribution of prevalence. source here

sample_posterior_r_mcmc_testun(samps, pos, n, tp, tn, fp, fn)

Arguments

samps	number of MCMC samples desired
pos	number of positive tests population
n	number of total tests in population
tp	true positive tests in the lab
tn	true negative tests in the lab
fp	false positive tests in the lab
fn	false negative tests in the lab

Value

Prevalence posterior distribution

Functions

• sample_posterior_r_mcmc_testun: one population - unknown test performance - posterior distribution of prevalence. source here

References

Larremore, D. B., Fosdick, B. K., Zhang, S., & Grad, Y. H. (2020). Jointly modeling prevalence, sensitivity and specificity for optimal sample allocation. bioRxiv. doi: https://doi.org/10.1101/2020.05.23.112649

Examples

if (FALSE) {

library(tidyverse)
library(skimr)

sensitivity = 0.93
specificity = 0.975
positive_pop <- c(321, 123, 100, 10)
negative_pop <- c(1234, 500, 375, 30)

positive_pop[1]/negative_pop[1]
posi <- c(2485713, 692)
total <- c(11609844, 3212)
nega <- total - posi
posi/total
posi/nega
nt <- 2
result_unk <- sample_posterior_r_mcmc_testun(samps = 10000,
                                             #in population
                                             pos = posi[nt], #positive_pop[1], #positive
                                             # n = nega[nt], #negative_pop[1], #negatives
                                             n = total[nt], #negative_pop[1], #negatives
                                             # in lab
                                             tp = 30,tn = 50,fp = 0,fn = 0
                                             # tp = 670,tn = 640,fp = 202,fn = 74
                                             )

# reproducible example YY

result_unk %>%
  as_tibble() %>%
  skim()

result_unk %>%
  as_tibble() %>%
  ggplot(aes(x = r)) +
  geom_histogram(aes(y=..density..),binwidth = 0.005) +
  geom_density()

result_unk %>%
  as_tibble() %>%
  rownames_to_column() %>%
  pivot_longer(cols = -rowname,names_to = "estimates",values_to = "values") %>%
  ggplot(aes(x = values)) +
  geom_histogram(aes(y=..density..),binwidth = 0.005) +
  geom_density() +
  facet_grid(~estimates,scales = "free_x")
}