AUCjmcs: Time-dependent AUC for joint models

In FastJM: Semi-Parametric Joint Modeling of Longitudinal and Survival Data

Time-dependent AUC for joint models

Description

Time-dependent AUC for joint models

Usage

AUCjmcs(
  seed = 100,
  object,
  landmark.time = NULL,
  horizon.time = NULL,
  obs.time = NULL,
  method = c("Laplace", "GH"),
  quadpoint = NULL,
  maxiter = NULL,
  n.cv = 3,
  survinitial = TRUE,
  ...
)

Arguments

seed	a numeric value of seed to be specified for cross validation.
object	object of class 'jmcs'.
landmark.time	a numeric value of time for which dynamic prediction starts..
horizon.time	a numeric vector of future times for which predicted probabilities are to be computed.
obs.time	a character string of specifying a longitudinal time variable.
method	estimation method for predicted probabilities. If Laplace, then the empirical empirical estimates of random effects is used. If GH, then the pseudo-adaptive Gauss-Hermite quadrature is used.
quadpoint	the number of pseudo-adaptive Gauss-Hermite quadrature points if method = "GH".
maxiter	the maximum number of iterations of the EM algorithm that the function will perform. Default is 10000.
n.cv	number of folds for cross validation. Default is 3.
survinitial	Fit a Cox model to obtain initial values of the parameter estimates. Default is TRUE.
...	Further arguments passed to or from other methods.

Value

a list of matrices with conditional probabilities for subjects.

Author(s)

Shanpeng Li [e-mail]

See Also

jmcs, survfitjmcs

FastJM documentation built on May 29, 2024, 8:39 a.m.