diagstats {diagmeta}R Documentation

Calculate statistical measures of test performance for objects of class diagmeta

Description

The user can provide cutoffs, sensitivities, and / or specificities to calculate the respective quantities (with confidence intervals). Furthermore, positive predictive values (PPV), negative predictive values (NPV), and probabilities of disease (PD) are calculated if the prevalence is provided.

Usage

diagstats(x, cutoff = x$optcut, sens, spec, prevalence, level = 0.95)

Arguments

x

An object of class diagmeta

cutoff

A numeric or vector with cutoff value(s)

sens

A numeric or vector with sensitivity value(s)

spec

A numeric or vector with specificity value(s)

prevalence

A numeric or vector with the prevalence(s)

level

The level used to calculate confidence intervals

Value

A data frame of class "diagstats" with the following variables:

cutoff

Cutoffs provided in argument "cutoff" and / or model-based cutoff values for given sensitivities / specificities.

Sens

Sensitivities provided in argument "sens" and / or model-based estimates of the sensitivity for given cutoffs / specificities

seSens

Standard error of sensitivity

lower.Sens, upper.Sens

Lower and upper confidence limits of the sensitivity

Spec

Specificities provided in argument "spec" and / or model-based estimates of the specificity for given cutoffs / sensitivities

seSpec

Standard error of specificity

lower.Spec, upper.Spec

Lower and upper confidence limits of the specificity

prevalence

As defined above.

PPV

Positive predictive value (based on the cutoff)

NPV

Negative predictive value (based on the cutoff)

PD

Probability of disease if the given cutoff value was observed as the measurement for an individual

dens.nondiseased

Value of the model-based density function at the cutoff(s) for non-diseased individuals

dens.diseased

Value of the model-based density function at the cutoff(s) for diseased individuals

Author(s)

Gerta Rücker ruecker@imbi.uni-freiburg.de, Srinath Kolampally kolampal@imbi.uni-freiburg.de, Guido Schwarzer sc@imbi.uni-freiburg.de

See Also

diagmeta print.diagstats

Examples

# FENO dataset
#
data(Schneider2017)

diag1 <- diagmeta(tpos, fpos, tneg, fneg, cutpoint,
                  studlab = paste(author, year, group),
                  data = Schneider2017, 
                  log.cutoff = TRUE)

# Results at the optimal cutoff
#
diagstats(diag1)

# Results for cutoffs 25 and 50 (and a prevalence of 10%)
#
diagstats(diag1, c(25, 50), prevalence = 0.10)

# Results for sensitivity and specificity of 0.95
#
diagstats(diag1, sens = 0.95, spec = 0.95)


[Package diagmeta version 0.4-1 Index]