Imperfect serological test

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Imperfect test

Function correct_prevalence() is used for estimating the true prevalence if the serological test used is imperfect

Arguments:

data the input data frame, must either have:
- age, pos, tot columns (for aggregated data)
- OR age, status columns for (linelisting data)
- Users can specifiy the name for these columns in the input data frame using arguments age_col, pos_col, tot_color age_col, status_col respectively
bayesian whether to adjust sero-prevalence using the Bayesian or frequentist approach. If set to TRUE, true sero-prevalence is estimated using MCMC.
init_se sensitivity of the serological test (default value 0.95)
init_sp specificity of the serological test (default value 0.8)
study_size_se (applicable when bayesian=TRUE) sample size for sensitivity validation study (default value 1000)
study_size_sp (applicable when bayesian=TRUE) sample size for specificity validation study (default value 1000)
chains (applicable when bayesian=TRUE) number of Markov chains (default to 1)
warmup (applicable when bayesian=TRUE) number of warm up runs (default value 1000)
iter (applicable when bayesian=TRUE) number of iterations (default value 2000)

The function will return a list of 2 items:

info
- if bayesian = TRUE contains estimated values for se, sp and corrected seroprevalence
- else return the formula for computing corrected seroprevalence
corrected_sero return a data.frame with age, sero (corrected sero) and pos, tot (adjusted based on corrected prevalence)

# ---- estimate real prevalence using Bayesian approach ----
data <- rubella_uk_1986_1987
output <- correct_prevalence(data, warmup = 1000, iter = 4000, init_se=0.9, init_sp = 0.8, study_size_se=1000, study_size_sp=3000)
#> 
#> SAMPLING FOR MODEL 'prevalence_correction' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 5.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.52 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 4000 [  0%]  (Warmup)
#> Chain 1: Iteration:  400 / 4000 [ 10%]  (Warmup)
#> Chain 1: Iteration:  800 / 4000 [ 20%]  (Warmup)
#> Chain 1: Iteration: 1001 / 4000 [ 25%]  (Sampling)
#> Chain 1: Iteration: 1400 / 4000 [ 35%]  (Sampling)
#> Chain 1: Iteration: 1800 / 4000 [ 45%]  (Sampling)
#> Chain 1: Iteration: 2200 / 4000 [ 55%]  (Sampling)
#> Chain 1: Iteration: 2600 / 4000 [ 65%]  (Sampling)
#> Chain 1: Iteration: 3000 / 4000 [ 75%]  (Sampling)
#> Chain 1: Iteration: 3400 / 4000 [ 85%]  (Sampling)
#> Chain 1: Iteration: 3800 / 4000 [ 95%]  (Sampling)
#> Chain 1: Iteration: 4000 / 4000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.161 seconds (Warm-up)
#> Chain 1:                0.323 seconds (Sampling)
#> Chain 1:                0.484 seconds (Total)
#> Chain 1:

# check fitted value 
output$info[1:2, ]
#>             mean      se_mean          sd      2.5%       25%       50%
#> est_se 0.9279110 1.030783e-04 0.006108971 0.9156867 0.9238106 0.9280891
#> est_sp 0.8028227 9.624223e-05 0.006862949 0.7894737 0.7981445 0.8029028
#>              75%     97.5%    n_eff      Rhat
#> est_se 0.9321756 0.9391947 3512.383 1.0000643
#> est_sp 0.8074229 0.8163628 5084.991 0.9996717

# ---- estimate real prevalence using frequentist approach ----
freq_output <- correct_prevalence(data, bayesian = FALSE, init_se=0.9, init_sp = 0.8)

# check info
freq_output$info
#> [1] "Formula: real_sero = (observed_sero + sp - 1) / (se + sp -1)"

User can then visualize the output using plot_corrected_prev() function

# Plot output of the frequentist approach
plot_corrected_prev(freq_output)


# Plot output of the bayesian approach 
plot_corrected_prev(output)

To compare both correction methods in a single plot, provide the output from the second method as the optional y argument in plot_corrected_prev()

plot_corrected_prev(output, freq_output)


# set facet = TRUE to display the confidence or credible intervals for each method
plot_corrected_prev(output, freq_output, facet = TRUE)

Fitting corrected data

Data after seroprevalence correction

Bayesian approach

suppressWarnings(
  corrected_data <- farrington_model(
  output$corrected_se,
  start=list(alpha=0.07,beta=0.1,gamma=0.03))
)

plot(corrected_data)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.

Frequentist approach

suppressWarnings(
  corrected_data <- farrington_model(
  freq_output$corrected_se,
  start=list(alpha=0.07,beta=0.1,gamma=0.03))
)

plot(corrected_data)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.

Original data

suppressWarnings(
  original_data <- farrington_model(
  data,
  start=list(alpha=0.07,beta=0.1,gamma=0.03))
)
plot(original_data)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.

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They may not be fully stable and should be used with caution. We make no claims about them.
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