# ipd2diag: Individual participant data to enter them into diagmeta In diagmeta: Meta-Analysis of Diagnostic Accuracy Studies with Several Cutpoints

## Description

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

## Usage

 `1` ```ipd2diag(studlab, value, status) ```

## 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)

## Value

A data frame with values that can be entered into `diagmeta`.

## Author(s)

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

```diagmeta, plot.diagmeta, print.diagmeta, summary.diagmeta```
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34``` ```# 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(TP, FP, TN, FN, cutoff, studlab, data = 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") ```