Calculate covariance matrix in the MVN-ICAR

Description

Calculate covariance matrix in the MVN-ICAR

Usage

.ICAR_calc(s, J, clam)

Arguments

Value

Sigma_s = (I - W)^(-1) * Q, W, Q

RJMCMC (with Bayesian Borrowing)

Description

Metropolis-Hastings Green Reversible Jump move, with Bayesian Borrowing

Usage

.J_RJMCMC(
  df_hist,
  df_curr,
  Y,
  Y_0,
  I,
  I_0,
  X,
  X_0,
  lambda,
  lambda_0,
  beta,
  beta_0,
  mu,
  sigma2,
  tau,
  s,
  J,
  Jmax,
  bp,
  bp_0,
  clam_smooth,
  a_tau = NULL,
  b_tau = NULL,
  c_tau = NULL,
  d_tau = NULL,
  type,
  p_0 = NULL,
  phi,
  pi_b,
  maxSj
)

Arguments

Value

list of proposed J and s, with adjusted values of lambda, lambda_0, tau, Sigma_s, and data_frames for historical and current trial data.

RJMCMC (without Bayesian Borrowing)

Description

Metropolis-Hastings Green Reversible Jump move, without Bayesian Borrowing

Usage

.J_RJMCMC_NoBorrow(
  df,
  Y_0,
  I_0,
  X_0,
  lambda_0,
  beta_0,
  mu,
  sigma2,
  s,
  J,
  Jmax,
  bp_0,
  clam_smooth,
  phi,
  pi_b
)

Arguments

Value

list of proposed J and s, with adjusted values of lambda, lambda_0, tau, Sigma_s, and data_frames for historical and current trial data

Beta MH RW sampler from freq PEM fit

Description

Sample beta from RW sampler

Usage

.beta.MH.RW.glm(df, beta, beta_count, cprop_beta)

Arguments

Value

beta, either old or new move

Proposal beta with a Metropolis Adjusted Langevin (MALA)

Description

Proposal beta with a Metropolis Adjusted Langevin (MALA)

Usage

.beta_MH_MALA(df, beta, bp, cprop_beta, beta_count)

Arguments

Value

updated beta vector

Newton Raphson MH move

Description

Sample beta from RW sampler

Usage

.beta_MH_NR(df, beta, bp, cprop_beta, beta_count)

Arguments

Value

updated beta

Beta Metropolis-Hastings random walk move

Description

Update beta via a Metropolis-Hastings Random Walk move

Usage

.beta_MH_RW(df, beta, bp, cprop_beta, beta_count)

Arguments

Value

beta, either old or new move

Mean for MALA using derivative for beta proposal

Description

Mean for MALA using derivative for beta proposal

Usage

.beta_mom(df, k, beta, bp, cprop_beta)

Arguments

Value

proposal mean

First and second derivative of target for mode and variance of proposal

Description

First and second derivative of target for mode and variance of proposal

Usage

.beta_mom.NR.fun(df, k, beta, bp, cprop_beta)

Arguments

Value

First and second derivative mode and variance

Birth move in RJMCMC

Description

Calculates new values of x when proposing another split point, based on a weighted mean, as x_new/x <- (1-U)/U

Usage

.birth_move(U, sj, s_star, sjm1, x, j)

Arguments

Value

vector with adjusted parameter values after additional split point, length J + 2

Create data.frame for piecewise exponential models

Description

Construct a split data.frame for updated split points

Usage

.dataframe_fun(Y, I, X, s, lambda, bp, J)

Arguments

Value

data.frame with columns c(tstart, id, X1,..., Xp, Y, I, lambda)

Death move in RJMCMC

Description

Calculates new values of x when proposing the death of a split point

Usage

.death_move(sjp1, sj, sjm1, x, j)

Arguments

Value

vector with adjusted parameter values after removal of split point, length J

Fit frequentist piecewise exponential model for MLE and information matrix of beta

Description

Compute MLE for PEM

Usage

.glmFit(df)

Arguments

Value

beta MLE and inverse of information matrix

Input checker

Description

Checks inputs before Gibbs sampler is run

Usage

.input_check(
  Y,
  Y_0,
  X,
  X_0,
  tuning_parameters,
  initial_values = NULL,
  hyperparameters
)

Arguments

Value

a print statement

Lambda_0 MH step, proposal from conditional conjugate posterior

Description

Lambda_0 MH step, proposal from conditional conjugate posterior

Usage

.lambda_0_MH_cp(
  df_hist,
  Y_0,
  I_0,
  X_0 = NULL,
  s,
  beta_0 = NULL,
  mu,
  sigma2,
  lambda,
  lambda_0,
  tau,
  bp_0 = 0,
  J,
  clam,
  a_lam = 0.01,
  b_lam = 0.01,
  lambda_0_count = 0,
  lambda_0_move = 0
)

Arguments

Value

list of updated (if accepted) lambda_0 and data.frames, as well as the number of accepted moves

Lambda_0 MH step, proposal from conditional conjugate posterior

Description

Lambda_0 MH step, proposal from conditional conjugate posterior

Usage

.lambda_0_MH_cp_NoBorrow(
  df_hist,
  Y_0,
  I_0,
  X_0 = NULL,
  s,
  beta_0 = NULL,
  mu,
  sigma2,
  lambda_0,
  bp_0 = 0,
  J,
  clam,
  a_lam = 0.01,
  b_lam = 0.01,
  lambda_0_count = 0,
  lambda_0_move = 0
)

Arguments

Value

list of updated (if accepted) lambda_0 and data.frames, as well as the number of accepted moves

Lambda MH step, proposal from conditional conjugate posterior

Description

Lambda MH step, proposal from conditional conjugate posterior

Usage

.lambda_MH_cp(
  df_hist,
  df_curr,
  Y,
  I,
  X,
  s,
  beta,
  beta_0 = NULL,
  mu,
  sigma2,
  lambda,
  lambda_0,
  tau,
  bp,
  bp_0 = 0,
  J,
  a_lam = 0.01,
  b_lam = 0.01,
  lambda_move = 0,
  lambda_count = 0,
  alpha = 0.3
)

Arguments

Value

list of updated (if accepted) lambda and data.frames, as well as the number of accepted moves

Propose lambda from a gamma conditional conjugate posterior proposal

Description

Propose lambda from a gamma conditional conjugate posterior proposal

Usage

.lambda_conj_prop(df, beta, j, bp, alam = 0.01, blam = 0.01)

Arguments

Value

list containing proposed lambda, shape and rate parameters

Calculate log gamma ratio for two different parameter values

Description

Calculate log gamma ratio for two different parameter values

Usage

.lgamma_ratio(x1, x2, shape, rate)

Arguments

Value

log gamma ratio

Loglikelihood ratio calculation for beta parameters

Description

Compute log likelihood for beta update

Usage

.llikelihood_ratio_beta(df, beta, beta_new)

Arguments

Value

likelihood ratio

Log likelihood for lambda / lambda_0 update

Description

Log likelihood for lambda / lambda_0 update

Usage

.llikelihood_ratio_lambda(df, df_prop, beta)

Arguments

Value

log likelihood ratio for lambda

Log likelihood function

Description

Log likelihood function

Usage

.log_likelihood(df, beta)

Arguments

Value

log likelihood given lambdas and betas

Computes the logarithmic sum of an exponential

Description

Computes the logarithmic sum of an exponential

Usage

.logsumexp(x)

Arguments

Value

the logarithmic sum of an exponential

log Gaussian proposal density for Newton Raphson proposal

Description

log Gaussian proposal density for Newton Raphson proposal

Usage

.lprop.dens.beta.NR(beta.prop, mu_old, var_old)

Arguments

Value

log Gaussian density

Log density of proposal for MALA

Description

Log density of proposal for MALA

Usage

.lprop_density_beta(beta_prop, mu, cprop_beta)

Arguments

Value

log density

Calculate log density tau prior

Description

Calculate log density tau prior

Usage

.ltau_dprior(tau, a_tau, b_tau, c_tau = NULL, d_tau = NULL, p_0 = NULL, type)

Arguments

Value

log density of tau

Calculate mu posterior update

Description

Calculate mu posterior update

Usage

.mu_update(Sigma_s, lambda_0, sigma2, J)

Arguments

Value

mu update from Normal.

Normalize a set of probability to one, using the the log-sum-exp trick

Description

Normalize a set of probability to one, using the the log-sum-exp trick

Usage

.normalize_prob(x)

Arguments

Value

normalized set of log probabilities

Calculates nu and sigma2 for the Gaussian Markov random field prior, for a given split point j

Description

Calculates nu and sigma2 for the Gaussian Markov random field prior, for a given split point j

Usage

.nu_sigma_update(j, lambda_0, mu, sigma2, W, Q, J)

Arguments

Value

nu and sigma2

Plot histogram from MCMC samples

Description

Plots a histogram of the given discrete MCMC samples

Usage

.plot_hist(
  samples,
  title = "",
  xlab = "Values",
  ylab = "Frequency",
  color = "black",
  fill = "blue",
  binwidth = 0.05,
  scale_x = FALSE
)

Arguments

Value

a ggplot2 object

Plot smoothed baseline hazards

Description

Plot mean and given quantiles of a matrix. Can also be used to plot derivatives of the baseline hazard, such as estimated cumulative hazard and survival function.

Usage

.plot_matrix(
  x_lim,
  y,
  percentiles = c(0.05, 0.95),
  title = "",
  xlab = "",
  ylab = "",
  color = "blue",
  fill = "blue",
  linewidth = 1,
  alpha = 0.2,
  y2 = NULL,
  color2 = "red",
  fill2 = "red"
)

Arguments

Value

a ggplot2 object

Plot MCMC trace

Description

Creates a trace plot of given MCMC samples.

Usage

.plot_trace(
  x_lim,
  samples,
  title = "",
  xlab = "",
  ylab = "",
  color = "black",
  linewidth = 1
)

Arguments

Value

a ggplot2 object

Predictive hazard from BayesFBHborrow object

Description

Predictive hazard from BayesFBHborrow object

Usage

.predictive_hazard(out_slam, x_pred, beta_samples)

Arguments

Value

matrix of the predictive hazard

Predictive hazard ratio (HR) from BayesFBHborrow object

Description

Predictive hazard ratio (HR) from BayesFBHborrow object

Usage

.predictive_hazard_ratio(x_pred, beta_samples)

Arguments

Value

posterior samples for expectation and credible intervals

Predictive survival from BayesFBHborrow object

Description

Predictive survival from BayesFBHborrow object

Usage

.predictive_survival(grid_width, out_slam, x_pred, beta_samples)

Arguments

Value

matrix of the predictive survival

Set tuning parameters

Description

Set tuning parameters

Usage

.set_hyperparameters(hyperparameters = NULL, model_choice)

Arguments

Value

filled list of tuning_parameters

Set tuning parameters

Description

Set tuning parameters

Usage

.set_tuning_parameters(tuning_parameters = NULL, borrow, X, X_0 = NULL)

Arguments

Value

filled list of tuning_parameters

Metropolis Hastings step: shuffle the split point locations (with Bayesian borrowing)

Description

Metropolis Hastings step: shuffle the split point locations (with Bayesian borrowing)

Usage

.shuffle_split_point_location(
  df_hist,
  df_curr,
  Y_0,
  I_0,
  X_0,
  lambda_0,
  beta_0,
  Y,
  I,
  X,
  lambda,
  beta,
  s,
  J,
  bp_0,
  bp,
  clam_smooth,
  maxSj
)

Arguments

Value

list containing new split points, updated Sigma_s and data.frames for historic and current trial data

Metropolis Hastings step: shuffle the split point locations (without Bayesian borrowing)

Description

Metropolis Hastings step: shuffle the split point locations (without Bayesian borrowing)

Usage

.shuffle_split_point_location_NoBorrow(
  df,
  Y_0,
  I_0,
  X_0,
  lambda_0,
  beta_0,
  s,
  J,
  bp_0,
  clam_smooth
)

Arguments

Value

list containing new split points, updated Sigma_s and data.frames for historic and current trial data

Calculate sigma2 posterior update

Description

Calculate sigma2 posterior update

Usage

.sigma2_update(mu, lambda_0, Sigma_s, J, a_sigma, b_sigma)

Arguments

Value

sigma2 draw from IG

Smoothed hazard function

Description

Smoothed hazard function

Usage

.smooth_hazard(out_slam, beta_samples = NULL)

Arguments

Value

smoothed function for the baseline hazard

Smoothed survival curve

Description

Smoothed survival curve

Usage

.smooth_survival(grid_width, out_slam, beta_samples = NULL)

Arguments

Value

smoothed survival function

Sample tau from posterior distribution

Description

Sample tau from posterior distribution

Usage

.tau_update(
  lambda_0,
  lambda,
  J,
  s,
  a_tau,
  b_tau,
  c_tau = NULL,
  d_tau = NULL,
  p_0 = NULL,
  type
)

Arguments

Value

list containing tau and new mixture ratio

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

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))

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

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)

Run the MCMC sampler with Bayesian Borrowing

Description

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

Usage

## S3 method for class 'WBorrow'
BayesFBHborrow(
  data,
  data_hist,
  borrow = TRUE,
  model_choice = "mix",
  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

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))

S3 generic, calls the correct GibbsMH sampler

Description

An MCMC sampler for Bayesian borrowing with time-to-event data. We obtain a flexible baseline hazard function by making the split points random within a piecewise exponential model and using a Gaussian Markov random field prior to smooth the baseline hazards. Only calls the sampler and does not run any input checks. Best practice is to call BayesFBHborrow(), if the user is not familiar with the model at hand.

Usage

GibbsMH(
  Y,
  I,
  X,
  Y_0 = NULL,
  I_0 = NULL,
  X_0 = NULL,
  tuning_parameters,
  hyperparameters,
  lambda_hyperparameters,
  iter,
  warmup_iter,
  refresh,
  max_grid
)

Arguments

Value

depending on if the user wishes to borrow; returns a list with values after each iteration for parameters: out_fixed (J, mu, sigma2, beta), lambda, lambda_0, tau, s, as well as tuning values of the total number of accepts: lambda_move, lambda_0_move and beta_move. Also included is the out_slam which contains the shrunk estimate of the baseline hazard.

Examples

set.seed(123)
# Load example data and set your initial values and hyper parameters
data(weibull_cc, package = "BayesFBHborrow")
data(weibull_hist, package = "BayesFBHborrow")

# The datasets consists of 3 (2) columns named "tte", "event" and "X" 
# (only for concurrent). To explicitly run the sampler, extract the samples as
# following
Y <- weibull_cc$tte
I <- weibull_cc$event
X <- matrix(weibull_cc$X_trt)

Y_0 <- weibull_hist$tte
I_0 <- weibull_hist$event
X_0 <- NULL

# Specify 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)
                          
output <- GibbsMH(Y, I, X, Y_0, I_0, X_0,
                  tuning_parameters, hyper, 
                  iter = 5, warmup_iter = 1)

GibbsMH sampler, without Bayesian Borrowing

Description

An MCMC sampler for time-to-event data, without Bayesian Borrowing. We obtain a flexible baseline hazard function by making the split points random within a piecewise exponential model and using a Gaussian Markov random field prior to smooth the baseline hazards. Only calls the sampler and does not run any input checks. Best practice is to call BayesFBHborrow(), if the user is not familiar with the model at hand.

Usage

## S3 method for class 'NoBorrow'
GibbsMH(
  Y,
  I,
  X = NULL,
  Y_0 = NULL,
  I_0 = NULL,
  X_0 = NULL,
  tuning_parameters,
  hyperparameters = list(a_sigma = 1, b_sigma = 1, phi = 3, clam_smooth = 0.8),
  lambda_hyperparameters = list(a_lambda = 0.01, b_lambda = 0.01),
  iter = 1500L,
  warmup_iter = 10L,
  refresh = 0,
  max_grid = 2000L
)

Arguments

Value

list with values after each iteration for parameters: out_fixed (J, mu, sigma2, beta), lambda, s, as well as tuning values of the total number of accepts: lambda_move and beta_move. Also included is the out_slam which contains the shrunk estimate of the baseline hazard.

Examples

set.seed(123)
# Load example data and set your hyper parameters
data(weibull_cc, package = "BayesFBHborrow")
data(weibull_hist, package = "BayesFBHborrow")

# The datasets consists of 3 (2) columns named "tte", "event" and "X".
# To explicitly run the sampler, extract the samples as following
Y <- weibull_cc$tte
I <- weibull_cc$event
X <- matrix(weibull_cc$X_trt)

# Specify hyperparameters and tuning parameters
hyper <-  list("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)
                          
# Set initial values to 'NULL' for default settings
output <- GibbsMH(Y, I, X, NULL, NULL, NULL,
                  tuning_parameters = tuning_parameters, hyperparameters = hyper, 
                  iter = 5, warmup_iter = 1)

GibbsMH sampler, with Bayesian Borrowing

Description

An MCMC sampler for Bayesian borrowing with time-to-event data. We obtain a flexible baseline hazard function by making the split points random within a piecewise exponential model and using a Gaussian Markov random field prior to smooth the baseline hazards. Only calls the sampler and does not run any input checks. Best practice is to call BayesFBHborrow(), if the user is not familiar with the model at hand.

Usage

## S3 method for class 'WBorrow'
GibbsMH(
  Y,
  I,
  X,
  Y_0,
  I_0,
  X_0,
  tuning_parameters = NULL,
  hyperparameters = list(a_tau = 1, b_tau = 0.001, c_tau = 1, d_tau = 1, type = "mix",
    p_0 = 0.8, a_sigma = 1, b_sigma = 1, phi = 3, clam_smooth = 0.8),
  lambda_hyperparameters = list(a_lambda = 0.01, b_lambda = 0.01),
  iter = 150L,
  warmup_iter = 10L,
  refresh = 0,
  max_grid = 2000L
)

Arguments

Value

list with values after each iteration for parameters: out_fixed (J, mu, sigma2, beta), lambda, lambda_0, tau, s, as well as tuning values of the total number of accepts: lambda_move, lambda_0_move and beta_move. Also included is the out_slam which contains the shrunk estimate of the baseline hazard.

Examples

set.seed(123)
# Load example data and set your initial values and hyper parameters
data(weibull_cc, package = "BayesFBHborrow")
data(weibull_hist, package = "BayesFBHborrow")

# The datasets consists of 3 (2) columns named "tte", "event" and "X" 
# (only for concurrent). To explicitly run the sampler, extract the samples as
# following
Y <- weibull_cc$tte
I <- weibull_cc$event
X <- matrix(weibull_cc$X_trt)

Y_0 <- weibull_hist$tte
I_0 <- weibull_hist$event
X_0 <- NULL

# Specify 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)
          
output <- GibbsMH(Y, I, X, Y_0, I_0, X_0, tuning_parameters = tuning_parameters,
                  hyperparameters = hyper, iter = 5, warmup_iter = 1)

Extract mean posterior values

Description

S3 method for class "BayesFBHborrow", returns the mean posterior values for the fixed parameters

Usage

## S3 method for class 'BayesFBHborrow'
coef(object, ...)

Arguments

Value

mean values of given samples

Examples

data(weibull_cc, package = "BayesFBHborrow")

# Set your tuning parameters
tuning_parameters <- list("Jmax" = 5,
                          "pi_b" = 0.5,
                          "cprop_beta" = 0.5)
                          
# run the MCMC sampler
out <- BayesFBHborrow(weibull_cc, NULL, tuning_parameters = tuning_parameters,
                      iter = 3, warmup_iter = 1)

# Plot the posterior mean values of the fixed parameters
coef(out$out)

Create group level data

Description

Aggregate individual level data into group level data

Usage

group_summary(Y, I, X, s)

Arguments

Value

list of group level data

Examples

set.seed(111)
# Load example data and set your initial values and hyper parameters
data(weibull_cc, package = "BayesFBHborrow")
data(weibull_hist, package = "BayesFBHborrow")

Y <- weibull_cc$tte
I <- weibull_cc$event
X <- weibull_cc$X_trt

# Say we want to know the group level data for the following split points
s <- quantile(Y, c(0, 0.45, 0.65, 1), names = FALSE)

group_summary(Y, I, X, s)

Initialize lambda hyperparameters

Description

Propose lambda hyperparameters for the choice of initial values for lambda

Usage

init_lambda_hyperparameters(group_data, s, w = 0.5)

Arguments

Value

shape and rate for the estimated lambda distribution

Examples

set.seed(111)
# Load example data and set your initial values and hyper parameters
data(weibull_cc, package = "BayesFBHborrow")
data(weibull_hist, package = "BayesFBHborrow")

Y <- weibull_cc$tte
I <- weibull_cc$event
X <- weibull_cc$X_trt

# Say we want to know the group level data for the following split points
s <- quantile(Y, c(0, 0.45, 0.65, 1), names = FALSE)

group_data <- group_summary(Y, I, NULL, s)
init_lambda_hyperparameters(group_data, s)

Example data, simulated from a piecewise exponential model.

Description

Data is simulated for a concurrent trial with three columns named "tte" (time-to-event), "event" (event indicator), and "X_trt" (treatment indicator). It was simulated using the following parameters:

Usage

data(piecewise_exp_cc)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 250 rows and 3 columns.

Examples

data(piecewise_exp_cc)
survival_model <- survival::survfit(survival::Surv(tte, event) ~ X_trt, data = piecewise_exp_cc)
line_colors <- c("blue", "red")  # Adjust colors as needed
line_types <- 1:length(unique(piecewise_exp_cc$X_trt))
plot(survival_model, col = line_colors, lty = line_types, 
     xlab = "Time (tte)", ylab = "Survival Probability", 
     main = "Kaplan-Meier Survival Curves by Treatment")

Example data, simulated from a piecewise exponential model.

Description

Data is simulated for a historical trial with two columns named "tte" (time-to-event) and "event" (event indicator). It was simulated using the following parameters:

Usage

data(piecewise_exp_hist)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 100 rows and 2 columns.

Examples

data(piecewise_exp_cc)
data(piecewise_exp_hist)
piecewise_exp_hist$X_trt <- 0
survival_model <- survival::survfit(survival::Surv(tte, event) ~ X_trt, 
                                    data = rbind(piecewise_exp_cc, 
                                    piecewise_exp_hist))
line_colors <- c("blue", "red", "green")  # Adjust colors as needed
line_types <- 1:length(unique(piecewise_exp_cc$X_trt))
plot(survival_model, col = line_colors, lty = line_types, 
     xlab = "Time (tte)", ylab = "Survival Probability", 
     main = "Kaplan-Meier Survival Curves by Treatment")

Plot the MCMC results

Description

S3 object which produces predictive probabilities of the survival, hazard, and hazard ratio for a given set of predictors

Usage

## S3 method for class 'BayesFBHborrow'
plot(x, x_lim, x_pred = NULL, ...)

Arguments

Value

nested list of 'plots' (posterior predictive hazard, survival, and hazard ratio) as well as their samples.

Examples

data(weibull_cc, package = "BayesFBHborrow")

# Set your tuning parameters
tuning_parameters <- list("Jmax" = 5,
                          "pi_b" = 0.5,
                          "cprop_beta" = 0.5)
                          
# run the MCMC sampler
out <- BayesFBHborrow(weibull_cc, NULL, tuning_parameters = tuning_parameters,
                      iter = 3, warmup_iter = 1)

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

Summarize fixed MCMC results

Description

S3 method for with borrowing. Returns summary of mean, median and given percentiles for the one dimensional parameters.

Usage

## S3 method for class 'BayesFBHborrow'
summary(
  object,
  estimator = NULL,
  percentiles = c(0.025, 0.25, 0.75, 0.975),
  ...
)

Arguments

Value

summary of the given estimator

Examples

data(piecewise_exp_cc, package = "BayesFBHborrow")

# Set your tuning parameters
tuning_parameters <- list("Jmax" = 5,
                          "pi_b" = 0.5,
                          "cprop_beta" = 0.5)
                          
# run the MCMC sampler
out <- BayesFBHborrow(piecewise_exp_cc, NULL, tuning_parameters = tuning_parameters,
                      iter = 3, warmup_iter = 1)

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

Example data, simulated from a Weibull distribution.

Description

Data is simulated for a concurrent trial with three columns named "tte" (time-to-event), "event" (event indicator), and "X_trt" (treatment indicator). It was simulated by drawing samples from a Weibull with kappa = 1.5 (shape) and nu = 0.4 (scale)

Usage

data(weibull_cc)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 250 rows and 3 columns.

Examples

data(weibull_cc)
survival_model <- survival::survfit(survival::Surv(tte, event) ~ X_trt, data = weibull_cc)
line_colors <- c("blue", "red")  # Adjust colors as needed
line_types <- 1:length(unique(weibull_cc$X_trt))
plot(survival_model, col = line_colors, lty = line_types, 
     xlab = "Time (tte)", ylab = "Survival Probability", 
     main = "Kaplan-Meier Survival Curves by Treatment")

Example data, simulated from a Weibull distribution

Description

Data is simulated for a historical trial with two columns named "tte" (time-to-event) and "event" (event indicator). It was simulated using the following parameters:

Usage

data(weibull_hist)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 100 rows and 2 columns.

Examples

data(weibull_cc)
data(weibull_hist)
weibull_hist$X_trt <- 0
survival_model <- survival::survfit(survival::Surv(tte, event) ~ X_trt, 
                                    data = rbind(weibull_cc, 
                                    weibull_hist))
line_colors <- c("blue", "red", "green")  # Adjust colors as needed
line_types <- 1:length(unique(weibull_cc$X_trt))
plot(survival_model, col = line_colors, lty = line_types, 
     xlab = "Time (tte)", ylab = "Survival Probability", 
     main = "Kaplan-Meier Survival Curves by Treatment")