Calculate an estimate of true prevalence from apparent prevalence, and uncertain estimates of test sensitivity and test specificity, using one of 3 methods.
Usage
true_prevalence(
pos_obs,
n_obs,
false_pos_controls = NULL,
n_controls = NULL,
false_neg_diseased = NULL,
n_diseased = NULL,
confint = 0.95,
method = c("lang-reiczigel", "rogan-gladen", "bayes"),
...,
spec = NULL,
sens = NULL
)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.
- confint
confidence interval limits
- method
one of:
"lang-reiczigel": Frequentist confidence limits: see
prevalence_lang_reiczigel()"rogan-gladen": Rogan gladen incorporating uncertainty with resampling: see
uncertain_rogan_gladen()"bayes": Bayesian analysis: see
bayesian_component_simpler_model()
- ...
Arguments passed on to
uncertain_rogan_gladensamplesnumber fo random draws of sensitivity and specificity
fmta
sprintfformatting string accepting 3 numbersseedset seed for reproducibility
- spec
the prior specificity of the test as a
beta_dist.- sens
the prior sensitivity of the test as a
beta_dist.
Value
A dataframe containing the following columns:
test (character) - the name of the test or panel
prevalence.lower (numeric) - the lower estimate
prevalence.median (numeric) - the median estimate
prevalence.upper (numeric) - the upper estimate
prevalence.method (character) - the method of estimation
prevalence.label (character) - a fomatted label of the true prevalence estimate with CI
Ungrouped.
No default value.
Examples
true_prevalence(c(1:50), 200, 2, 800, 25, 75)
#> # A tibble: 50 × 15
#> prevalence.lower prevalence.median prevalence.upper prevalence.method
#> <dbl> <dbl> <dbl> <chr>
#> 1 0 0.00373 0.0420 lang-reiczigel
#> 2 0 0.0112 0.0534 lang-reiczigel
#> 3 0 0.0188 0.0644 lang-reiczigel
#> 4 0.00313 0.0263 0.0751 lang-reiczigel
#> 5 0.00787 0.0338 0.0856 lang-reiczigel
#> 6 0.0128 0.0413 0.0959 lang-reiczigel
#> 7 0.0179 0.0488 0.106 lang-reiczigel
#> 8 0.0231 0.0564 0.116 lang-reiczigel
#> 9 0.0284 0.0639 0.126 lang-reiczigel
#> 10 0.0338 0.0714 0.136 lang-reiczigel
#> # ℹ 40 more rows
#> # ℹ 11 more variables: prevalence.label <chr>, spec.median <dbl>,
#> # spec.lower <dbl>, spec.upper <dbl>, spec.label <chr>, sens.median <dbl>,
#> # sens.lower <dbl>, sens.upper <dbl>, sens.label <chr>, pos_obs <int>,
#> # n_obs <dbl>
true_prevalence(c(1:10)*2, 200, 25, 800, 1, 6, method="rogan-gladen")
#> # A tibble: 10 × 15
#> prevalence.median prevalence.lower prevalence.upper prevalence.label
#> <dbl> <dbl> <dbl> <chr>
#> 1 0 0 0 0.00% [0.00% — 0.00%]
#> 2 0 0 0 0.00% [0.00% — 0.00%]
#> 3 0 0 0.0127 0.00% [0.00% — 1.27%]
#> 4 0.0117 0 0.0288 1.17% [0.00% — 2.88%]
#> 5 0.0244 0.00755 0.0498 2.44% [0.75% — 4.98%]
#> 6 0.0371 0.0220 0.0677 3.71% [2.20% — 6.77%]
#> 7 0.0495 0.0304 0.0930 4.95% [3.04% — 9.30%]
#> 8 0.0624 0.0433 0.105 6.24% [4.33% — 10.50%]
#> 9 0.0747 0.0562 0.131 7.47% [5.62% — 13.09%]
#> 10 0.0881 0.0659 0.157 8.81% [6.59% — 15.73%]
#> # ℹ 11 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>, pos_obs <dbl>, n_obs <dbl>
true_prevalence(c(1:10)*2, 200, 5, 800, 1, 6, method="bayes")
#>
#> SAMPLING FOR MODEL 'component-logit' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 1.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
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#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.088169 seconds (Warm-up)
#> Chain 1: 0.083953 seconds (Sampling)
#> Chain 1: 0.172122 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'component-logit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 9e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
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#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.078706 seconds (Warm-up)
#> Chain 2: 0.078158 seconds (Sampling)
#> Chain 2: 0.156864 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'component-logit' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 9e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
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#> Chain 3:
#> Chain 3: Elapsed Time: 0.076819 seconds (Warm-up)
#> Chain 3: 0.080375 seconds (Sampling)
#> Chain 3: 0.157194 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'component-logit' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
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#> Chain 4:
#> Chain 4: Elapsed Time: 0.082852 seconds (Warm-up)
#> Chain 4: 0.077246 seconds (Sampling)
#> Chain 4: 0.160098 seconds (Total)
#> Chain 4:
#> # A tibble: 10 × 15
#> prevalence.median prevalence.lower prevalence.upper prevalence.label
#> <dbl> <dbl> <dbl> <chr>
#> 1 0.00217 0.0000214 0.0210 0.22% [0.00% — 2.10%]
#> 2 0.0128 0.000546 0.0439 1.28% [0.05% — 4.39%]
#> 3 0.0250 0.00408 0.0657 2.50% [0.41% — 6.57%]
#> 4 0.0373 0.0117 0.0846 3.73% [1.17% — 8.46%]
#> 5 0.0497 0.0208 0.104 4.97% [2.08% — 10.43%]
#> 6 0.0625 0.0292 0.124 6.25% [2.92% — 12.35%]
#> 7 0.0751 0.0378 0.144 7.51% [3.78% — 14.40%]
#> 8 0.0873 0.0488 0.161 8.73% [4.88% — 16.10%]
#> 9 0.0982 0.0574 0.173 9.82% [5.74% — 17.28%]
#> 10 0.110 0.0651 0.192 11.02% [6.51% — 19.20%]
#> # ℹ 11 more variables: prevalence.method <chr>, sens.median <dbl>,
#> # sens.lower <dbl>, sens.upper <dbl>, sens.label <chr>, spec.median <dbl>,
#> # spec.lower <dbl>, spec.upper <dbl>, spec.label <chr>, pos_obs <dbl>,
#> # n_obs <dbl>