SimSurvNMarker (version 0.1.3)

sim_joint_data_set: Simulate Individuals from a Joint Survival and Marker Model

Description

Simulates individuals from the following model

$$\vec U_i \sim N^{(K)}(\vec 0, \Psi)$$ $$\vec Y_{ij} \mid \vec U_i = \vec u_i \sim N^{(r)}(\vec \mu_i(s_{ij}, \vec u_i), \Sigma)$$ $$h(t \mid \vec u) = \exp(\vec\omega^\top\vec b(t) + \vec\delta^\top\vec z_i + \vec 1^\top(diag(\vec \alpha) \otimes \vec x_i^\top)vec(\Gamma) + \vec 1^\top(diag(\vec \alpha) \otimes \vec g(t)^\top)vec(B) + \vec 1^\top(diag(\vec \alpha) \otimes \vec m(t)^\top)\vec u)$$

with

$$\vec\mu_i(s, \vec u) = (I \otimes \vec x_i^\top)vec(\Gamma) + \left(I \otimes \vec g(s)^\top\right)vec(B) + \left(I \otimes \vec m(s)^\top\right) \vec u$$

where \(h(t \mid \vec u)\) is the conditional hazard function.

Usage

sim_joint_data_set(
  n_obs,
  B,
  Psi,
  omega,
  delta,
  alpha,
  sigma,
  gamma,
  b_func,
  m_func,
  g_func,
  r_z,
  r_left_trunc,
  r_right_cens,
  r_n_marker,
  r_x,
  r_obs_time,
  y_max,
  use_fixed_latent = TRUE,
  m_func_surv = m_func,
  g_func_surv = g_func,
  gl_dat = get_gl_rule(30L),
  tol = .Machine$double.eps^(1/4)
)

Arguments

n_obs

integer with the number of individuals to draw.

B

coefficient matrix for time-varying fixed effects. Use NULL if there is no effect.

Psi

the random effects' covariance matrix.

omega

numeric vector with coefficients for the baseline hazard.

delta

coefficients for fixed effects in the log hazard.

alpha

numeric vector with association parameters.

sigma

the noise's covariance matrix.

gamma

coefficient matrix for the non-time-varying fixed effects. Use NULL if there is no effect.

b_func

basis function for the baseline hazard like poly.

m_func

basis function for U like poly.

g_func

basis function for B like poly.

r_z

generator for the covariates in the log hazard. Takes an integer for the individual's id.

r_left_trunc

generator for the left-truncation time. Takes an integer for the individual's id.

r_right_cens

generator for the right-censoring time. Takes an integer for the individual's id.

r_n_marker

function to generate the number of observed markers. Takes an integer for the individual's id.

r_x

generator for the covariates in for the markers. Takes an integer for the individual's id.

r_obs_time

function to generate the observations times given the number of observed markers. Takes an integer for the number of markers and an integer for the individual's id.

y_max

maximum survival time before administrative censoring.

use_fixed_latent

logical for whether to include the \(\vec 1^\top(diag(\vec \alpha) \otimes \vec x_i^\top)vec(\Gamma)\) term in the log hazard. Useful if derivatives of the latent mean should be used.

m_func_surv

basis function for U like poly in the log hazard. Can be different from m_func. Useful if derivatives of the latent mean should be used.

g_func_surv

basis function for B like poly in the log hazard. Can be different from g_func. Useful if derivatives of the latent mean should be used.

gl_dat

Gauss–Legendre quadrature data. See get_gl_rule.

tol

convergence tolerance passed to uniroot.

See Also

See the examples on Github at https://github.com/boennecd/SimSurvNMarker/tree/master/inst/test-data or this vignette vignette("SimSurvNMarker", package = "SimSurvNMarker").

sim_marker and surv_func_joint

Examples

#####
# example with polynomial basis functions
b_func <- function(x){
  x <- x - 1
  cbind(x^3, x^2, x)
}
g_func <- function(x){
  x <- x - 1
  cbind(x^3, x^2, x)
}
m_func <- function(x){
  x <- x - 1
  cbind(x^2, x, 1)
}

# parameters
delta <- c(-.5, -.5, .5)
gamma <- matrix(c(.25, .5, 0, -.4, 0, .3), 3, 2)
omega <- c(1.4, -1.2, -2.1)
Psi <- structure(c(0.18, 0.05, -0.05, 0.1, -0.02, 0.06, 0.05, 0.34, -0.25,
                   -0.06, -0.03, 0.29, -0.05, -0.25, 0.24, 0.04, 0.04,
                   -0.12, 0.1, -0.06, 0.04, 0.34, 0, -0.04, -0.02, -0.03,
                   0.04, 0, 0.1, -0.08, 0.06, 0.29, -0.12, -0.04, -0.08,
                   0.51), .Dim = c(6L, 6L))
B <- structure(c(-0.57, 0.17, -0.48, 0.58, 1, 0.86), .Dim = 3:2)
sig <- diag(c(.6, .3)^2)
alpha <- c(.5, .9)

# generator functions
r_n_marker <- function(id)
  # the number of markers is Poisson distributed
  rpois(1, 10) + 1L
r_obs_time <- function(id, n_markes)
  # the observations times are uniform distributed
  sort(runif(n_markes, 0, 2))
r_z <- function(id)
  # return a design matrix for a dummy setup
  cbind(1, (id %% 3) == 1, (id %% 3) == 2)
r_x <- r_z # same covariates for the fixed effects
r_left_trunc <- function(id)
  # no left-truncation
  0
r_right_cens <- function(id)
  # right-censoring time is exponentially distributed
  rexp(1, rate = .5)

# simulate
gl_dat <- get_gl_rule(30L)
y_max <- 2
n_obs <- 100L
set.seed(1)
dat <- sim_joint_data_set(
  n_obs = n_obs, B = B, Psi = Psi, omega = omega, delta = delta,
  alpha = alpha, sigma = sig, gamma = gamma, b_func = b_func,
  m_func = m_func, g_func = g_func, r_z = r_z, r_left_trunc = r_left_trunc,
  r_right_cens = r_right_cens, r_n_marker = r_n_marker, r_x = r_x,
  r_obs_time = r_obs_time, y_max = y_max)

# checks
stopifnot(
  NROW(dat$survival_data) == n_obs,
  NROW(dat$marker_data) >= n_obs,
  all(dat$survival_data$y <= y_max))