.shuffle_split_point_location {BayesFBHborrow}

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

df_hist

dataframe containing historical trial data and parmaeters

df_curr

data.frame containing current trial data and parameters

Y_0

historical trial data

I_0

historical trial censoring indicator

X_0

historical trial design matrix

lambda_0

historical baseline hazard

beta_0

historical parameter vector

Y

data

I

censoring indicator

X

design matrix

lambda

baseline hazard

beta

parameter vector

s

split point locations, J + 2

J

number of split points

bp_0

number of covariates in historical trial

bp

number of covariates in current trial

clam_smooth

neighbor interactions, in range (0, 1), for ICAR update

maxSj

the smallest of the maximal time points, min(max(Y), max(Y_0))

Value

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