dcurves (version 0.5.1)

test_consequences: Test Consequences

Description

Test Consequences

Usage

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
)

Value

a tibble with test consequences

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.

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)