diagstats: 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)

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

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

Author

Gerta Rücker gerta.ruecker@uniklinik-freiburg.de, Srinath Kolampally kolampal@imbi.uni-freiburg.de, Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de

Examples

Run this code

# 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)