predict.mvna {mvna}R Documentation

Calculates Nelson-Aalen estimates at specified time-points

Description

This function gives the Nelson-Aalen estimates at time-points specified by the user, along with the two variance estimators.

Usage

## S3 method for class 'mvna':
predict(object, times, tr.choice, ...)

Arguments

object An object of class 'mvna'
times Time-points at which you want the estimates
tr.choice A vector of character giving for which transitions you want estimates. By default, the function will give the Nelson-Aalen estimates for all the transitions.
... Other arguments to predict

Value

Returns a list named after the possible transitions, e.g. if we define a multistate model with two possible transitions: from state 0 to state 1, and from state 0 to state 2, the returned list will have two parts named "0 1" and "0 2". Each part contains a data.frame with columns:

na Nelson-Aalen estimates at each transition times.
var1 Variance estimator given in eq. (4.1.6) of Andersen et al. (1993).
var2 Variance estimator given in eq. (4.1.7) of Andersen et al. (1993).
time The given timepoints.

Author(s)

Arthur Allignol, arthur.allignol@fdm.uni-freiburg.de

References

Andersen, P.K., Borgan, O., Gill, R.D. and Keiding, N. (1993). Statistical models based on counting processes. Springer Series in Statistics. New York, NY: Springer.

See Also

mvna, sir.adm, sir.cont

Examples

data(sir.cont)

# 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
    }
  }
}

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

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

# Using predict
predict(na,times=c(1,5,10,15))

[Package mvna version 1.1-8 Index]