surv_func_joint: Evaluates the Conditional Survival Function Given the Random...

In SimSurvNMarker: Simulate Survival Time and Markers

Evaluates the Conditional Survival Function Given the Random Effects

Description

Evaluates the conditional survival function given the random effects, \vec U. The conditional hazard function is

h(t \mid \vec u) = \exp(\vecω^\top\vec b(t) + δ + \vecα^\top\vec o + \vec 1^\top(diag(\vec α) \otimes \vec g(t)^\top)vec(B) + \vec 1^\top(diag(\vec α) \otimes \vec m(t)^\top)\vec u).

Usage

surv_func_joint(
  ti,
  B,
  U,
  omega,
  delta,
  alpha,
  b_func,
  m_func,
  gl_dat = get_gl_rule(30L),
  g_func,
  offset
)

Arguments

ti	numeric vector with time points.
B	coefficient matrix for time-varying fixed effects. Use NULL if there is no effect.
U	random effects matrix for time-varying random effects. Use NULL if there is no effects.
omega	numeric vector with coefficients for the baseline hazard.
delta	offset on the log hazard scale. Use NULL if there is no effect.
alpha	numeric vector with association parameters.
b_func	basis function for the baseline hazard like poly.
m_func	basis function for U like poly.
gl_dat	Gauss–Legendre quadrature data. See get_gl_rule.
g_func	basis function for B like poly.
offset	numeric vector with non-time-varying fixed effects.

See Also

sim_marker, draw_U, eval_surv_base_fun

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
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)
alpha <- c(.5, .9)

# simulate and draw survival curve
gl_dat <- get_gl_rule(30L)
set.seed(1)
U <- draw_U(chol(Psi), NCOL(B))
tis <- seq(0, 2, length.out = 100)
Survs <- surv_func_joint(ti = tis, B = B, U = U, omega = omega,
                         delta = NULL, alpha = alpha, b_func = b_func,
                         m_func = m_func, gl_dat = gl_dat, g_func = g_func,
                         offset = NULL)
par_old <- par(mar = c(5, 5, 1, 1))
plot(tis, Survs, xlab = "Time", ylab = "Survival", type = "l",
     ylim = c(0, 1), bty = "l", xaxs = "i", yaxs = "i")
par(par_old)

SimSurvNMarker documentation built on Nov. 10, 2022, 5:12 p.m.