BayesFBHborrow.NoBorrow {BayesFBHborrow}

Run the MCMC sampler without Bayesian Borrowing

Description

Main function of the BayesFBHborrow package. This generic function calls the correct MCMC sampler for time-to-event without Bayesian borrowing.

Usage

## S3 method for class 'NoBorrow'
BayesFBHborrow(
  data,
  data_hist = NULL,
  borrow = FALSE,
  model_choice = "no_borrow",
  tuning_parameters = NULL,
  hyperparameters = NULL,
  lambda_hyperparameters = list(a_lambda = 0.01, b_lambda = 0.01),
  iter = 2000,
  warmup_iter = 2000,
  refresh = 0,
  verbose = FALSE,
  max_grid = 2000
)

Arguments

data

data.frame containing atleast three vectors of "tte" (time-to-event) and "event" (event indicator), and covariates "X_i" (where i should be a number/ indicator of the covariate)

data_hist

NULL (not used)

borrow

FALSE (default), will run the model with borrowing

model_choice

'no_borrow' (default), for no borrowing

tuning_parameters

list of "cprop_beta", "Jmax", and "pi_b". Default is ("Jmax" = 5, "cprop_beta" = 0.5, "pi_b" = 0.5)

hyperparameters

list containing the hyperparameters c("a_sigma", "b_sigma", "phi", clam_smooth"). Default is list("a_sigma" = 2, "b_sigma" = 2, "phi" = 3 , "clam_smooth" = 0.8)

lambda_hyperparameters

contains two hyperparameters ("a_lambda" and "b_lambda") used for the update of lambda, default is c(0.01, 0.01)

iter

number of iterations for MCMC sampler. Default is 2000

warmup_iter

number of warmup iterations (burn-in) for MCMC sampler. Default is 2000

refresh

number of iterations between printed console updates. Default is 0

verbose

FALSE (default), choice of output, if TRUE will output intermittent results into console

max_grid

grid size for the smoothed baseline hazard. Default is 2000

Value

a nested list of two items, 'out' and 'plots'. The list 'out' will contain all the samples of the MCMC chain, as well as acceptance ratios. The latter, 'plots', contains plots (and data) of the smoothed baseline hazard, smoothed survival, a histogram of the sampled number of split points, and the trace plot of the treatment effect beta_1

Examples

set.seed(123)
# Load the example data
data(piecewise_exp_cc, package = "BayesFBHborrow")

# Set your tuning parameters
tuning_parameters <- list("Jmax" = 5,
                          "cprop_beta" = 3.25)
                          
# Set initial values to default
out <- BayesFBHborrow(piecewise_exp_cc, NULL, borrow = FALSE, 
                      tuning_parameters = tuning_parameters,
                      iter = 2, warmup_iter = 0)