sir.cont {mvna} | R Documentation |
Ventilation status in intensive care unit patients
Description
Time-dependent ventilation status for intensive care unit (ICU) patients, a random sample from the SIR-3 study.
Usage
data(sir.cont)
Format
A data frame with 1141 rows and 6 columns:
- id:
Randomly generated patient id
- from:
State from which a transition occurs
- to:
State to which a transition occurs
- time:
Time when a transition occurs
- age:
Age at inclusion
- sex:
Sex.
F
for female andM
for male
The possible states are:
0: No ventilation
1: Ventilation
2: End of stay.
And cens
stands for censored observations.
Details
This data frame consists in a random sample of the SIR-3 cohort data. It focuses on the effect of ventilation on the length of stay (combined endpoint discharge/death). Ventilation status is considered as a transcient state in an illness-death model.
The data frame is directly formated to be used with the mvna
function, i.e., it is transition-oriented with one row per transition.
Source
Beyersmann, J., Gastmeier, P., Grundmann, H., Baerwolff, S., Geffers, C., Behnke, M., Rueden, H., and Schumacher, M. Use of multistate models to assess prolongation of intensive care unit stay due to nosocomial infection. Infection Control and Hospital Epidemiology, 27:493-499, 2006.
Examples
data(sir.cont)
# Matrix of possible transitions
tra <- matrix(ncol=3,nrow=3,FALSE)
tra[1, 2:3] <- TRUE
tra[2, c(1, 3)] <- TRUE
# Modification for patients entering and leaving a state
# at the same date
sir.cont <- sir.cont[order(sir.cont$id, sir.cont$time), ]
for (i in 2:nrow(sir.cont)) {
if (sir.cont$id[i]==sir.cont$id[i-1]) {
if (sir.cont$time[i]==sir.cont$time[i-1]) {
sir.cont$time[i-1] <- sir.cont$time[i-1] - 0.5
}
}
}
# Computation of the Nelson-Aalen estimates
na.cont <- mvna(sir.cont,c("0","1","2"),tra,"cens")
if (require("lattice")) {
xyplot(na.cont,tr.choice=c("0 2","1 2"),aspect=1,
strip=strip.custom(bg="white",
factor.levels=c("No ventilation -- Discharge/Death",
"Ventilation -- Discharge/Death"),
par.strip.text=list(cex=0.9)),
scales=list(alternating=1),xlab="Days",
ylab="Nelson-Aalen estimates")
}