predict.mvna: Calculates Nelson-Aalen estimates at specified time-points

In mvna: Nelson-Aalen Estimator of the Cumulative Hazard in Multistate Models

Description

This function gives the Nelson-Aalen estimates at time-points specified by the user.

Usage

## S3 method for class 'mvna'
predict(object, times, tr.choice, level = 0.95,
        var.type = c("aalen", "greenwood"),
        ci.fun = c("log", "linear", "arcsin"), ...)

Arguments

object	An object of class mvna
times	Time-points at which one wants the estimates
tr.choice	A vector of character giving for which transitions one wants estimates. By default, the function will give the Nelson-Aalen estimates for all transitions.
level	Level of the pointwise confidence intervals. Default is 0.95.
var.type	Variance estimator displayed and used to compute the pointwise confidence intervals. One of "aalen" or "greenwood". Default is "aalen".
ci.fun	Which transformation to apply for the confidence intervals. Choices are "linear", "log" or "arcsin". Default is "log".
...	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:

times	Time points specified by the user.
na	Nelson-Aalen estimates at the specified times.
var.aalen or var.greenwood	Depending on what was specified in var.type.
lower	Lower bound of the pointwise confidence intervals.
upper	Upper bound.

Author(s)

Arthur Allignol, [e-mail]

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, summary.mvna

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))

Example output

$`0 1`
   time         na    var.aalen      lower      upper n.risk n.event
1     1 0.02997275 8.166962e-05 0.01659891 0.05412196    367      11
5     5 0.10045818 2.831896e-04 0.07234298 0.13950000    283       3
10   10 0.19365022 7.770252e-04 0.14604643 0.25677045    148       2
15   15 0.25917549 1.319048e-03 0.19693079 0.34109413    106       2

$`0 2`
   time        na   var.aalen     lower     upper n.risk n.event
1     1 0.0000000 0.000000000 0.0000000 0.0000000    367       0
5     5 0.6235546 0.001870272 0.5443013 0.7143475    283      48
10   10 1.4590061 0.006234131 1.3121786 1.6222631    148      21
15   15 2.1043120 0.011616216 1.9033267 2.3265208    106      15

$`1 0`
   time        na    var.aalen      lower     upper n.risk n.event
1     1 0.1000000 0.0002631579 0.07276405 0.1374305    380      38
5     5 0.3830045 0.0011586942 0.32177564 0.4558842    285      16
10   10 0.6984301 0.0025564650 0.60604084 0.8049040    194      10
15   15 0.9812933 0.0043966392 0.85957175 1.1202515    129       4

$`1 2`
   time         na    var.aalen      lower     upper n.risk n.event
1     1 0.00000000 0.0000000000 0.00000000 0.0000000    380       0
5     5 0.07959526 0.0002553912 0.05370131 0.1179749    285       9
10   10 0.20606305 0.0008339679 0.15657028 0.2712008    194       8
15   15 0.33273804 0.0016902982 0.26117263 0.4239135    129       5

mvna documentation built on May 1, 2019, 10:16 p.m.