Test Consequences — test_consequences • dcurves

Test Consequences

test_consequences(
  formula,
  data,
  statistics = c("pos_rate", "neg_rate", "test_pos_rate", "test_neg_rate", "tp_rate",
    "fp_rate", "fn_rate", "tn_rate", "ppv", "npv", "sens", "spec", "lr_pos", "lr_neg"),
  thresholds = seq(0, 1, by = 0.25),
  label = NULL,
  time = NULL,
  prevalence = NULL
)

Arguments

formula: a formula with the outcome on the LHS and a sum of markers/covariates to test on the RHS
data: a data frame containing the variables in formula=.
statistics: Character vector with statistics to return. See below for details
thresholds: vector of threshold probabilities between 0 and 1. Default is seq(0, 0.99, by = 0.01). Thresholds at zero are replaced with 10e-10.
label: named list of variable labels, e.g. list(age = "Age, years")
time: if outcome is survival, time= specifies the time the assessment is made
prevalence: When NULL, the prevalence is estimated from data=. If the data passed is a case-control set, the population prevalence may be set with this argument.

Value

a tibble with test consequences

statistics

The following diagnostic statistics are available to return.

Statistic	Abbreviation	Definition
Outcome Positive Rate	`"pos_rate"`	`(a + c) / (a + b + c + d)`
Outcome Negative Rate	`"neg_rate"`	`(b + d) / (a + b + c + d)`
Test Positive Rate	`"test_pos_rate"`	`(a + b) / (a + b + c + d)`
Test Negative Rate	`"test_neg_rate"`	`(c + d) / (a + b + c + d)`
True Positive Rate	`"tp_rate"`	`a / (a + b + c + d)`
False Positive Rate	`"fp_rate"`	`b / (a + b + c + d)`
False Negative Rate	`"fn_rate"`	`c / (a + b + c + d)`
True Negative Rate	`"tn_rate"`	`d / (a + b + c + d)`
Positive Predictive Value	`"ppv"`	`a / (a + b)`
Negative Predictive Value	`"npv"`	`d / (c + d)`
Sensitivity	`"sens"`	`a / (a + c)`
Specificity	`"spec"`	`d / (b + d)`
Positive Likelihood Ratio	`"lr_pos"`	`sens / (1 - spec)`
Negative Likelihood Ratio	`"lr_neg"`	`(1 - sens) / spec`

Examples

test_consequences(cancer ~ cancerpredmarker, data = df_binary)
#> # A tibble: 5 × 18
#>   variable   label     n threshold pos_rate neg_rate test_pos_rate test_neg_rate
#>   <chr>      <fct> <int>     <dbl>    <dbl>    <dbl>         <dbl>         <dbl>
#> 1 cancerpre… canc…   750      0        0.14     0.86        1              0    
#> 2 cancerpre… canc…   750      0.25     0.14     0.86        0.192          0.808
#> 3 cancerpre… canc…   750      0.5      0.14     0.86        0.0667         0.933
#> 4 cancerpre… canc…   750      0.75     0.14     0.86        0.0187         0.981
#> 5 cancerpre… canc…   750      1        0.14     0.86        0              1    
#> # ℹ 10 more variables: tp_rate <dbl>, fp_rate <dbl>, fn_rate <dbl>,
#> #   tn_rate <dbl>, ppv <dbl>, npv <dbl>, sens <dbl>, spec <dbl>, lr_pos <dbl>,
#> #   lr_neg <dbl>