simeventhistories {simMSM} | R Documentation |
Simulate Event Histories
Description
Simulates n individual event histories.
Usage
simeventhistories(n, mpl, max.time, change.times, X, states.at.origin = NULL,
Xstruc, partial.markov.x = NULL, partial.markov.eta = NULL)
Arguments
n |
number of individuals. |
mpl |
model parameter list as generated (only a skeleton that has
to be suitably completed) by the function |
max.time |
maximum entry time. |
change.times |
vector giving the times of change of the time-change covariates. |
X |
design matrix. |
states.at.origin |
state-types at origin (default is all possible entry state-types, which is internally calculated). |
Xstruc |
X structure matrix. See Examples for more information. |
partial.markov.x |
function defining how the partial Markov covariates are generated (see example below). |
partial.markov.eta |
list of lists (as generated by the function
|
Details
The example below provides an intuitive description of how to use the different input arguments. The idea of partial Markov covariates is based on the definition in Commenges (1991). A description of this idea directly in the context of illness-death models is described on pp. 224-225 in Beyersmann et al. (1999).
Value
Three data frames named msm.bascis
, ttsce
,
tt.indicators
are returned organized within one list.
The three data frames and their respective variables will be described
in the next lines.
msm.bascis
contains the following variables variables:
id |
id (1, ..., n) of the individual |
entry |
entry times |
exit |
exit times |
from |
values of initial states |
to |
values of final states |
delta |
non-censoring indicator function |
x1 |
values of first covariate (additional covariates follow). If partial Markov objects are supplied, the generated covariates are attached as additional variables. |
The second data frame ttsce
contains a transition-type specific
covariate expansion (as well for partial Markov covariates in the case
of a partial Markov set-up).
The third data frame tt.indicators
contains the values of
transition-type indicator functions. For censored observations, all
values of one data line are equal to zero (as e.g. needed in a BayesX
full likelihood analysis).
Author(s)
Holger Reulen
References
Daniel Commenges (1991) Multi-state Models in Epidemiology. Lifetime Data Analysis, Vol. 5, No. 4.
Jan Beyersmann, Martin Schumacher, Arthur Allignol (2012) Competing Risks and Multistate Models with R. Springer Series 'UseR!'.
See Also
Examples
## An example for a time-varying setup without partial Markov effects:
tra2 <- matrix(ncol = 2, nrow = 2, data = FALSE)
tra2[1, 2] <- tra2[2, 1] <- TRUE
mpl <- mplskeleton(tmat = tra2)
mpl[[1]]$bhr[[2]] <- mpl[[2]]$bhr[[1]] <- function(t){return(0.5)}
mpl[[1]]$eta[[2]] <- function(x.i, t){ ## time-varying x2 and time-varying f(x2)
ifelse(t < 5,
return(1.0 * x.i[1] + 0.5 * x.i[2]),
return(1.0 * x.i[1] + 1.0 * x.i[3]))}
mpl[[2]]$eta[[1]] <- function(x.i, t){ ## time-varying x2 and time-varying f(x1)
ifelse(t < 5,
return(-0.5 * x.i[1] + 0.5 * x.i[2]),
return( 1.0 * x.i[1] + 0.5 * x.i[3]))}
set.seed(123)
N <- 2
X <- matrix(nrow = N, ncol = 2, rnorm(2 * N))
X <- cbind(X, X[, 2] + runif(N)/10)
colnames(X) <- c("x1", "x2.t1", "x2.t2")
Xstruc <- matrix(ncol = 2, nrow = 2, data = 0)
rownames(Xstruc) <- c("t1", "t2")
colnames(Xstruc) <- c("x1", "x2")
Xstruc[, 1] <- 1
Xstruc[, 2] <- c(2, 3)
d <- simeventhistories(n = N, mpl = mpl, X = X, max.time = 10,
change.times = c(5), Xstruc = Xstruc)
head(d$msm.basics)
## Not run:
## An Illness-Death model example with time-varying setup and partial Markov
## effects:
traIDM <- matrix(nrow = 3, ncol = 3, FALSE)
traIDM[1, 2] <- traIDM[1, 3] <- traIDM[2, 1] <- traIDM[2, 3] <- TRUE
mpl <- mplskeleton(tmat = traIDM)
mpl[[1]]$bhr[[2]] <- mpl[[1]]$bhr[[3]] <- mpl[[2]]$bhr[[1]] <-
mpl[[2]]$bhr[[3]] <- function(t){0.25}
mpl[[1]]$eta[[2]] <- mpl[[1]]$eta[[3]] <- mpl[[2]]$eta[[1]] <-
mpl[[2]]$eta[[3]] <- function(x.i, t){
ifelse(t < 5,
return(0.5 * x.i[1]),
return(0.5 * x.i[2]))}
set.seed(123)
N <- 500
X <- matrix(nrow = N, ncol = 1, rnorm(N))
X <- cbind(X, X[, 1] + rnorm(N)/10)
colnames(X) <- c("x1.t1", "x1.t2")
Xstruc <- matrix(ncol = 1, nrow = 2, data = 0)
rownames(Xstruc) <- c("t1", "t2")
colnames(Xstruc) <- c("x1")
Xstruc[, 1] <- c(1, 2)
Xstruc
## Now set-up the partial Markov influences:
## Function 'partial.markov.x' has to take 5 input arguments representig vectors
## of past history information. They have to take names 'entry', 'exit', 'from',
## 'to', and 'delta':
partial.markov.x <- function(entry, exit, from, to, delta){
count.12 <- sum(as.numeric((from == 1) & (to == 2) & (delta == 1)))
count.21 <- sum(as.numeric((from == 2) & (to == 1) & (delta == 1)))
return(c(count.12, count.21))}
## List 'partial.markov.eta' is a list of lists in analogy to 'mpl':
partial.markov.eta <- pmeskeleton(traIDM)
partial.markov.eta[[1]][[2]] <- function(x){return( 0.25 * x[1])}
partial.markov.eta[[1]][[3]] <- function(x){return( 0.50 * x[1])}
partial.markov.eta[[2]][[1]] <- function(x){return(-0.50 * x[1] + 0.25 * x[2])}
partial.markov.eta[[2]][[3]] <- function(x){return(0)}
## Event history simulation:
d <- simeventhistories(n = N, mpl = mpl, X = X, max.time = 10,
change.times = c(5), Xstruc = Xstruc,
partial.markov.x = partial.markov.x,
partial.markov.eta = partial.markov.eta)
## End(Not run)