Simulate Patients from Posterior Predictive Distribution

Usage

# S3 method for class 'JointModelSamples'
simulate(
  object,
  nsim = NULL,
  seed = NULL,
  newdata = NULL,
  times = c(-2, 0, 10, 50, 100),
  jitter_var = c(0, 0),
  time_max = 2000,
  time_step = 1,
  lambda_censor = 1/3000,
  scaled_variance = FALSE,
  ...
)

Arguments

object: (JointModelSamples) the object containing the posterior samples.
nsim: ignored.
seed: ignored.
newdata: (data.frame) A data frame containing data in the same format as the object@data@survival@data. Importantly, it should contain the same covariates and factor levels as the variables used in the survival formula object@data@survival@formula and the same columns used for study, id, and arm.
times: (numeric) times to simulate SLD for all patients.
jitter_var: (numeric) variances to add noise to the observed SLD times. The first value is for any times less than 0 and the second for any times after 0. All positive (negative) times will remain positive (negative). Jitter values are generated from a normal distribution with mean 0 and the given variances.
time_max: (number) the maximum time to simulate to.
time_step: (number) the time interval between evaluating the log-hazard function.
lambda_censor: (number) the censoring rate, as the parameter of an exponential distribution.
scaled_variance: (flag) Should variance be scaled by the expected value. Must be set the same as was used for model fitting.
...: unused Unused.

Details

Simulates a set of patients based on the covariates of those used in the model fit or from newdata, which must contain the same column names and factor levels.