Simulate data from a joint model
This function simulates multivariate longitudinal and time-to-event data from a joint model.
simData( n = 100, ntms = 5, beta = rbind(c(1, 1, 1, 1), c(1, 1, 1, 1)), gamma.x = c(1, 1), gamma.y = c(0.5, -1), sigma2 = c(1, 1), D = NULL, df = Inf, model = "intslope", theta0 = -3, theta1 = 1, censoring = TRUE, censlam = exp(-3), truncation = TRUE, trunctime = (ntms - 1) + 0.1 )
n |
the number of subjects to simulate data for. |
ntms |
the maximum number of (discrete) time points to simulate repeated longitudinal measurements at. |
beta |
a matrix of |
gamma.x |
a vector of |
gamma.y |
a vector of |
sigma2 |
a vector of |
D |
a positive-definite matrix specifying the variance-covariance
matrix. If |
df |
a non-negative scalar specifying the degrees of freedom for the
random effects if sampled from a multivariate t-distribution. The
default is |
model |
follows the model definition in the |
theta0, theta1 |
parameters controlling the failure rate. See Details. |
censoring |
logical: if |
censlam |
a scale (> 0) parameter for an exponential distribution
used to simulate random censoring times for when |
truncation |
logical: if |
trunctime |
a truncation time for use when |
The function simData simulates data from a joint model,
similar to that performed in Henderson et al. (2000). It works by first
simulating multivariate longitudinal data for all possible follow-up times
using random draws for the multivariate Gaussian random effects and
residual error terms. Data can be simulated assuming either
random-intercepts only in each of the longitudinal sub-models, or
random-intercepts and random-slopes. Currently, all models must have the
same structure. The failure times are simulated from proportional hazards
time-to-event models using the following methodologies:
model="int"The baseline hazard function is specified to be an exponential distribution with
λ_0(t) = \exp{θ_0}.
Simulation is conditional on known time-independent effects, and the methodology of Bender et al. (2005) is used to simulate the failure time.
model="intslope"The baseline hazard function is specified to be a Gompertz distribution with
λ_0(t) = \exp{θ_0 + θ_1 t}.
In the usual representation of the Gompertz distribution, θ_1 is the shape parameter, and the scale parameter is equivalent to \exp(θ_0). Simulation is conditional on on a predictable (linear) time-varying process, and the methodology of Austin (2012) is used to simulate the failure time.
A list of 2 data.frames: one recording the requisite
longitudinal outcomes data, and one recording the time-to-event data.
Pete Philipson (peter.philipson1@newcastle.ac.uk) and Graeme L. Hickey (graemeleehickey@gmail.com)
Austin PC. Generating survival times to simulate Cox proportional hazards models with time-varying covariates. Stat Med. 2012; 31(29): 3946-3958.
Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models. Stat Med. 2005; 24: 1713-1723.
Henderson R, Diggle PJ, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000; 1(4): 465-480.
beta <- rbind(c(0.5, 2, 1, 1),
c(2, 2, -0.5, -1))
D <- diag(4)
D[1, 1] <- D[3, 3] <- 0.5
D[1, 2] <- D[2, 1] <- D[3, 4] <- D[4, 3] <- 0.1
D[1, 3] <- D[3, 1] <- 0.01
sim <- simData(n = 250, beta = beta, D = D, sigma2 = c(0.25, 0.25),
censlam = exp(-0.2), gamma.y = c(-.2, 1), ntms = 8)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.