BayesFBHborrow {BayesFBHborrow}

BayesFBHborrow: Run MCMC for a piecewise exponential model

Description

Main function of the BayesFBHborrow package. This generic function calls the correct MCMC sampler for time-to-event Bayesian borrowing.

Usage

BayesFBHborrow(
  data,
  data_hist = NULL,
  borrow = TRUE,
  model_choice,
  tuning_parameters,
  hyperparameters,
  lambda_hyperparameters,
  iter,
  warmup_iter,
  refresh,
  verbose,
  max_grid
)

Arguments

data

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

data_hist

data.frame containing atleast two vectors of "tte" (time-to-event) and "event" (censoring), with the option of adding covariates named "X_0_i" (where i should be a number/ indicator of the covariate), for historical data

borrow

TRUE (default), will run the model with borrowing

model_choice

choice of which borrowing model to use out of 'mix', 'uni' or 'all'

tuning_parameters

list of "cprop_beta" ("cprop_beta_0" for historical data), "alpha", "Jmax", and "pi_b". Default is ("Jmax" = 5, "clam_smooth" = 0.8, "cprop_beta" = 0.5, "cprop_beta_0" = 0.5, "pi_b" = 0.5)

hyperparameters

list containing the hyperparameters c("a_tau", "b_tau", "c_tau", "d_tau","type", "p_0", "a_sigma", "b_sigma"). Default is list("a_tau" = 1, "b_tau" = 1,"c_tau" = 1, "d_tau" = 0.001, "type" = "mix", "p_0" = 0.5, "a_sigma" = 2, "b_sigma" = 2, "phi" = 3)

lambda_hyperparameters

contains two hyperparameters (a_lambda and b_lambda) used for the update of lambda and lambda_0. Default is c(0.01, 0.01)

iter

number of iterations for MCMC sampler

warmup_iter

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

refresh

number of iterations between printed screen updates

verbose

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

max_grid

grids size for the smoothed baseline hazard

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")
data(piecewise_exp_hist, package = "BayesFBHborrow")

# Set your tuning parameters
tuning_parameters <- list("Jmax" = 5,
                          "pi_b" = 0.5,
                          "cprop_beta" = 3.25,
                          "alpha" = 0.4)
                          
# Set hyperparameters to default, with the borrowing model "mix"
out <- BayesFBHborrow(data = piecewise_exp_cc, data_hist = piecewise_exp_hist,
                      model_choice = 'mix', tuning_parameters = tuning_parameters,
                      iter = 2, warmup_iter = 0)

# Create a summary of the output
summary(out$out, estimator = "out_fixed")

# Plot the predictive curves for the treatment group
plots <- plot(out$out, out$out$time_grid, x_pred = c(1))