diagstats: Calculate statistical measures of test performance for...

In guido-s/diagmeta: Meta-Analysis of Diagnostic Accuracy Studies with Several Cutpoints

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.
lower.Sens, upper.Sens	Lower and upper confidence limits of the sensitivities.
Spec	Specificities provided in argument spec and / or model-based estimates of the specificity for given cutoffs / sensitivities.
lower.Spec, upper.Spec	Lower and upper confidence limits of the specificities.
prevalence	As defined above.
PPV	Positive predictive value (based on the prevalence).
lower.PPV, upper.PPV	Lower and upper confidence limits of positive predictive values.
NPV	Negative predictive value (based on the prevalence)
lower.NPV, upper.NPV	Lower and upper confidence limits of negative predictive values.
PD	Probability of disease if the given cutoff value was observed as the measurement for an individual.
lower.PD, upper.PD	Lower and upper confidence limits of probabilities of disease.
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 [e-mail], Srinath Kolampally [e-mail], Guido Schwarzer [e-mail]

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)

guido-s/diagmeta documentation built on Oct. 1, 2024, 6:29 p.m.