mvna (version 1.0-2)

sir.cont: 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 1161 rows and 4 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} The possible states are: 0:{No ventilation} 1:{Ventilation} 2:{End of stay}

And cens stands for censored observations.

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.

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.

Examples

data(sir.cont)

# Matrix of possible transitions
tra <- matrix(ncol=3,nrow=3,TRUE)
tra[3,1:2] <- FALSE

# Computation of the Nelson-Aalen estimates
  na.cont <- mvna(sir.cont,c("0","1","2"),tra,"cens")

  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")

## Not run: 
  # Bootstrap
  # Bootstrap statistics. Works for one transition at a time
  na.boot <- function(data,index,state.names,tra,cens.name,ctr) {
  temp <- mvna(data[index,],state.names,tra,
               cens.name,tr.choice=ctr)[[ctr]]$var1
  return(temp)
  }

  # Matrix of possible transitions
  tra <- matrix(ncol=3,nrow=3,TRUE)
  tra[3,1:2] <- FALSE

  # Test
  na.boot(sir.cont,1:nrow(sir.cont),
          c("0","1","2"),tra,"cens","0 2")

  library(boot)
  nb <- 10 ## nb=1000
  var.boot <- boot(sir.cont,na.boot,nb,
                   state.names=c("0","1","2"),
                   tra=tra,cens.name="cens",ctr="0 2",sim="permutation")

  m <- apply(var.boot$t,MARGIN=2,FUN=mean)
## End(Not run)