search for: tlat

..., size=N, replace=TRUE, prob=rep(1/M, M)) # Create vector of r.e effects re.effect <- rnorm(M,0,sigma) # Now create a vector which assigns the re.effect depending on the group re.group.effect <- re.effect[re.group] # Weibull latent event times v <- runif(n=N) Tlat <- round((- log(v) / (lambda * exp(x1 * beta1 + x2 * beta2 + x3 * beta3 + x4 * beta4 + re.group.effect)))^(1 / rho)) # censoring times #C <-rep(100000,N) C <- rexp(n=N, rate=rateC) # follow-up times and event indicators time <- pmin(Tlat, C) #status <- as.nu...

...e --> N Bernoulli trials x1 <- sample(x=c(0, 1), size=N, replace=TRUE, prob=c(0.5, 0.5)) # Now create random effect stuff # Create one vector of length N, all drawn from same normal distribution rand.effect <- rnorm(N,0,sigma) # Weibull latent event times v <- runif(n=N) Tlat <- round((- log(v) / (lambda * exp(x1 * beta1 + rand.effect)))^(1 / rho)) multiplier = exp(x1 * beta1 + rand.effect) haz=lambda * exp(x1 * beta1 + rand.effect) # censoring times #C <-rep(100000,N) C <- rexp(n=N, rate=rateC) # follow-up times and event indicators time <-...