# Simulate Event Histories Simulates n individual event histories. ```r 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 `mplskeleton` (see examples below). - `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 `pmeskeleton` in close analogy to `mpl`) defining how the partial Markov linear predictors are generated (see example below). ## 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). ## Returns 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 `mplskeleton` ## Examples ```r ## 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) ```

simeventhistories function