# The function that simulates independent/cluster-correlated semi-competing risks data under semi-Markov Weibull/Weibull-MVN models. The function to simulate independent/cluster-correlated semi-competing risks data under semi-Markov Weibull/Weibull-MVN models. ```r simID(id=NULL, x1, x2, x3, beta1.true, beta2.true, beta3.true, alpha1.true, alpha2.true, alpha3.true, kappa1.true, kappa2.true, kappa3.true, theta.true, SigmaV.true=NULL, cens) ``` ## Arguments - `id`: a vector of cluster information for `n` subjects. The cluster membership must be set to consecutive positive integers, $1:J$. Required only when generating clustered data. - `x1`: covariate matrix, `n` observations by `p1` variables. - `x2`: covariate matrix, `n` observations by `p2` variables. - `x3`: covariate matrix, `n` observations by `p3` variables. - `beta1.true`: true value for $\beta_1$. - `beta2.true`: true value for $\beta_2$. - `beta3.true`: true value for $\beta_3$. - `alpha1.true`: true value for $\alpha_1$. - `alpha2.true`: true value for $\alpha_2$. - `alpha3.true`: true value for $\alpha_3$. - `kappa1.true`: true value for $\kappa_1$. - `kappa2.true`: true value for $\kappa_2$. - `kappa3.true`: true value for $\kappa_3$. - `theta.true`: true value for $\theta$. - `SigmaV.true`: true value for $\Sigma_V$. Required only when generating clustered data. - `cens`: a vector with two numeric elements. The right censoring times are generated from Uniform($cens[1]$, $cens[2]$). ## Returns `simIDcor` returns a data.frame containing semi-competing risks outcomes from `n` subjects. It is of dimension $n\times 4$: the columns correspond to $y_1$, $\delta_1$, $y_2$, $\delta_2$. - **y1**: a vector of `n` times to the non-terminal event - **y2**: a vector of `n` times to the terminal event - **delta1**: a vector of `n` censoring indicators for the non-terminal event time (1=event occurred, 0=censored) - **delta2**: a vector of `n` censoring indicators for the terminal event time (1=event occurred, 0=censored) ## Author(s) Kyu Ha Lee and Sebastien Haneuse Maintainer: Kyu Ha Lee ## Examples ```r library(MASS) set.seed(123456) J = 110 nj = 50 n = J * nj id <- rep(1:J, each = nj) kappa1.true <- 0.05 kappa2.true <- 0.01 kappa3.true <- 0.01 alpha1.true <- 0.8 alpha2.true <- 1.1 alpha3.true <- 0.9 beta1.true <- c(0.5, 0.8, -0.5) beta2.true <- c(0.5, 0.8, -0.5) beta3.true <- c(1, 1, -1) SigmaV.true <- matrix(0.25,3,3) theta.true <- 0.5 cens <- c(90, 90) cov1 <- matrix(rnorm((length(beta1.true)-1)*n, 0, 1), n, length(beta1.true)-1) cov2 <- sample(c(0, 1), n, replace = TRUE) x1 <- as.data.frame(cbind(cov1, cov2)) x2 <- as.data.frame(cbind(cov1, cov2)) x3 <- as.data.frame(cbind(cov1, cov2)) simData <- simID(id, x1, x2, x3, beta1.true, beta2.true, beta3.true, alpha1.true, alpha2.true, alpha3.true, kappa1.true, kappa2.true, kappa3.true, theta.true, SigmaV.true, cens) ```

simID function