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,
  tuning_parameters,
  initial_values,
  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 three 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

tuning_parameters

list of "cprop_beta", "cprop_beta_0", "alpha", "Jmax", and "pi_b"

initial_values

list containing the initial values of c("J", "s_r", "mu", "sigma2", "tau", "lambda_0", "lambda", "beta_0", "beta") (optional)

hyperparameters

list containing the hyperparameters c("a_tau", "b_tau", "c_tau", "d_tau","type", "p_0", "a_sigma", "b_sigma", "Jmax", "clam_smooth", "cprop_beta", "phi", "pi_b"). 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, "Jmax" = 20, "clam_smooth" = 0.8, "cprop_beta" = 0.3, "phi" = 3, "pi_b" = 0.5)

lambda_hyperparameters

contains two (three) hyperparameters (a, b (,alpha)) used for the update of lambda and lambda_0. alpha is the power parameter when sampling for lambda (effects how much is borrowed)

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

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

max_grid

grids size for the smoothed baseline hazard

Value

list of samples for both fixed (can be found in $out_fixed) and multidimensional parameters (lambda, lambda_0, s, tau)

Examples

set.seed(123)
# Load the example data and write your initial values and hyper parameters
data(piecewise_exp_cc, package = "BayesFBHborrow")
data(piecewise_exp_hist, package = "BayesFBHborrow")

# Set your hyperparameters and tuning parameters
hyper <-  list("a_tau" = 1, 
               "b_tau" = 0.001,
               "c_tau" = 1,
               "d_tau" = 1, 
               "type" = "all",
               "p_0" = 0.5, 
               "a_sigma" = 2,
               "b_sigma" = 2,
               "clam_smooth" = 0.5,
               "phi" = 3)

tuning_parameters <- list("Jmax" = 5,
                          "pi_b" = 0.5,
                          "cprop_beta" = 0.5,
                          "alpha" = 0.4)
                          
# Set initial values to default
out <- BayesFBHborrow(piecewise_exp_cc, piecewise_exp_hist, tuning_parameters,
                      initial_values = NULL, hyper, iter = 5, warmup_iter = 1)

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

# Plot some of the estimates
# Do beta (trace), s (hist) and lambda (matrix)
trace <- plot(out, 1:5, estimator = "beta_1", type = "trace")
hist <- plot(out, estimator = "J", type = "hist")
smoothed_baseline_hazard <- plot(out, 1:2000, estimator = "out_slam", 
                                 type = "matrix")