ipd2diag: Individual participant data to enter them into diagmeta

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

Individual participant data to enter them into diagmeta

Description

Function to transform individual patient data (IPD) to enter them into diagmeta

Usage

ipd2diag(studlab, value, status, data = NULL, direction = "increasing")

Arguments

studlab	A vector with study labels.
value	A vector with individual patients' measurements of a discrete or continuous variable.
status	A vector with information of the individual's status (0 = non-diseased, 1 = diseased).
data	An optional data frame containing the study information.
direction	A character string specifying whether the probability of the target condition (e.g., a disease) is "increasing" or "decreasing" with higher values of the biomarker, can be abbreviated (see diagmeta).

Value

A data frame with additional class 'ipd2diag' containing the following variables:

studlab	As defined above.
cutoff	Cutoff values.
TP, FP, TN, FN	Number of true positives, false positives, true negatives and false negatives.

Author(s)

Gerta Rücker [e-mail], Srinath Kolampally [e-mail]

See Also

diagmeta, plot.diagmeta, print.diagmeta, summary.diagmeta

Examples

# Simulate IPD data for three studies, each with 30 patients based
# on normally distributed marker values
#
set.seed(20)
k <- 3
n <- 60
m <- c(20, 23, 26)
d <- 10
s <- 5
studlab <- c(rep(1, n), rep(2, n), rep(3, n))
status <- rep(c(rep(0, n / 2), rep(1, n / 2)), k)
measurement <- c(rnorm(n / 2, m[1], s), rnorm(n/2, m[1] + d, s),
                 rnorm(n / 2, m[2], s), rnorm(n/2, m[2] + d, s), 
                 rnorm(n / 2, m[3], s), rnorm(n/2, m[3] + d, s))
#
IPDdata <- data.frame(studlab, measurement, status)
str(IPDdata)

# Transform these data using ipd2diag()
#
diagdata <- ipd2diag(studlab, value = measurement, status = status)
str(diagdata)

# Run diagmeta()
#
diag1 <- diagmeta(diagdata, distr = "normal")
summary(diag1)
plot(diag1)
par(mfrow = c(1, 2))
plot(diag1, which = "ROC", lines = TRUE)
plot(diag1, which = "SROC", ciSens = TRUE,
  ciSpec = TRUE, lines = TRUE, shading = "hatch")

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