Provides a concise summary of the content of `MCTab`

objects. Computes
sensitivity, specificity, positive and negative predictive values and positive
and negative likelihood ratios for a diagnostic test with reference/gold standard.
Computes positive/negative percent agreement, overall percent agreement and Kappa
when the new test is evaluated by comparison to a non-reference standard. Computes
average positive/negative agreement when the both tests are all not the
reference, such as paired reader precision.

## Usage

```
getAccuracy(object, ...)
# S4 method for MCTab
getAccuracy(
object,
ref = c("r", "nr", "bnr"),
alpha = 0.05,
r_ci = c("wilson", "wald", "clopper-pearson"),
nr_ci = c("wilson", "wald", "clopper-pearson"),
bnr_ci = "bootstrap",
bootCI = c("perc", "norm", "basic", "stud", "bca"),
nrep = 1000,
rng.seed = NULL,
digits = 4,
...
)
```

## Arguments

- object
(

`MCTab`

)

input from diagTab function to create 2x2 contingency table.- ...
other arguments to be passed to DescTools::BinomCI.

- ref
(

`character`

)

reference condition. It is possible to choose one condition for your require. The`r`

indicates that the comparative test is standard reference,`nr`

indicates the comparative test is not a standard reference, and`bnr`

indicates both the new test and comparative test are not references.- alpha
(

`numeric`

)

type-I-risk, \(\alpha\).- r_ci
(

`string`

)

string specifying which method to calculate the confidence interval for a diagnostic test with reference/gold standard. Default is`wilson`

. Options can be`wilson`

,`wald`

and`clopper-pearson`

, see DescTools::BinomCI.- nr_ci
(

`string`

)

string specifying which method to calculate the confidence interval for the comparative test with non-reference standard. Default is`wilson`

. Options can be`wilson`

,`wald`

and`clopper-pearson`

, see DescTools::BinomCI.- bnr_ci
(

`string`

)

string specifying which method to calculate the confidence interval for both tests are not reference like reader precision. Default is`bootstrap`

. But when the point estimate of`ANA`

or`APA`

is equal to 0 or 100%, the method will be changed to`transformed wilson`

.- bootCI
(

`string`

)

string specifying the which bootstrap confidence interval from`boot.ci()`

function in`boot`

package. Default is`perc`

(bootstrap percentile), options can be`norm`

(normal approximation),`boot`

(basic bootstrap),`stud`

(studentized bootstrap) and`bca`

(adjusted bootstrap percentile).- nrep
(

`integer`

)

number of replicates for bootstrapping, default is 1000.- rng.seed
(

`integer`

)

number of the random number generator seed for bootstrap sampling. If set to NULL currently in the R session used RNG setting will be used.- digits
(

`integer`

)

the desired number of digits. Default is 4.

## Value

A data frame contains the qualitative diagnostic accuracy criteria with three columns for estimated value and confidence interval.

sens: Sensitivity refers to how often the test is positive when the condition of interest is present.

spec: Specificity refers to how often the test is negative when the condition of interest is absent.

ppv: Positive predictive value refers to the percentage of subjects with a positive test result who have the target condition.

npv: Negative predictive value refers to the percentage of subjects with a negative test result who do not have the target condition.

plr: Positive likelihood ratio refers to the probability of true positive rate divided by the false negative rate.

nlr: Negative likelihood ratio refers to the probability of false positive rate divided by the true negative rate.

ppa: Positive percent agreement, equals to sensitivity when the candidate method is evaluated by comparison with a comparative method, not reference/gold standard.

npa: Negative percent agreement, equals to specificity when the candidate method is evaluated by comparison with a comparative method, not reference/gold standard.

opa: Overall percent agreement.

kappa: Cohen's kappa coefficient to measure the level of agreement.

apa: Average positive agreement refers to the positive agreements and can be regarded as weighted ppa.

ana: Average negative agreement refers to the negative agreements and can be regarded as weighted npa.

## Examples

```
# For qualitative performance
data("qualData")
tb <- qualData %>%
diagTab(
formula = ~ CandidateN + ComparativeN,
levels = c(1, 0)
)
getAccuracy(tb, ref = "r")
#> EST LowerCI UpperCI
#> sens 0.8841 0.8200 0.9274
#> spec 0.8710 0.7655 0.9331
#> ppv 0.9385 0.8833 0.9685
#> npv 0.7714 0.6605 0.8541
#> plr 6.8514 3.5785 13.1181
#> nlr 0.1331 0.0832 0.2131
getAccuracy(tb, ref = "nr", nr_ci = "wilson")
#> EST LowerCI UpperCI
#> ppa 0.8841 0.8200 0.9274
#> npa 0.8710 0.7655 0.9331
#> opa 0.8800 0.8277 0.9180
#> kappa 0.7291 0.6283 0.8299
# For Between-Reader precision performance
data("PDL1RP")
reader <- PDL1RP$btw_reader
tb2 <- reader %>%
diagTab(
formula = Reader ~ Value,
bysort = "Sample",
levels = c("Positive", "Negative"),
rep = TRUE,
across = "Site"
)
getAccuracy(tb2, ref = "bnr")
#> EST LowerCI UpperCI
#> apa 0.9479 0.9266 0.9690
#> ana 0.9540 0.9342 0.9726
#> opa 0.9511 0.9311 0.9711
getAccuracy(tb2, ref = "bnr", rng.seed = 12306)
#> EST LowerCI UpperCI
#> apa 0.9479 0.9260 0.9686
#> ana 0.9540 0.9342 0.9730
#> opa 0.9511 0.9311 0.9711
```