# simData: Simulate data from a joint model In joineRML: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

 simData R Documentation

## Simulate data from a joint model

### Description

This function simulates multivariate longitudinal and time-to-event data from a joint model.

### Usage

```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
)
```

### Arguments

 `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 `dim=c(K,4)` specifying the coefficients of the fixed effects. The order in each row is intercept, time, a continuous covariate, and a binary covariate. `gamma.x` a vector of `length=2` specifying the coefficients for the time-to-event baseline covariates, in the order of a continuous covariate and a binary covariate. `gamma.y` a vector of `length=K` specifying the latent association parameters for each longitudinal outcome. `sigma2` a vector of `length=K` specifying the residual standard errors. `D` a positive-definite matrix specifying the variance-covariance matrix. If `model='int'`, the matrix has dimension `dim=c(K, K)`, else if `model='intslope'`, the matrix has dimension ```dim =c(2K, 2K)```. If `D=NULL` (default), an identity matrix is assumed. `df` a non-negative scalar specifying the degrees of freedom for the random effects if sampled from a multivariate t-distribution. The default is `df=Inf`, which corresponds to a multivariate normal distribution. `model` follows the model definition in the `joint` function. See Details for choices. `theta0, theta1` parameters controlling the failure rate. See Details. `censoring` logical: if `TRUE`, includes an independent censoring time. `censlam` a scale (> 0) parameter for an exponential distribution used to simulate random censoring times for when `censoring=TRUE`. `truncation` logical: if `TRUE`, adds a truncation time for a maximum event time. `trunctime` a truncation time for use when `truncation=TRUE`.

### Details

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.

### Value

A list of 2 `data.frame`s: one recording the requisite longitudinal outcomes data, and one recording the time-to-event data.

### Author(s)

Pete Philipson (peter.philipson1@newcastle.ac.uk) and Graeme L. Hickey (graemeleehickey@gmail.com)

### References

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.

### Examples

```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)
```

joineRML documentation built on Jan. 22, 2023, 1:18 a.m.