riAFTBART_fit {riAFTBART}

Fit a random effect accelerated failure time BART model

Description

This function implements the random effect accelerated failure time BART (riAFT-BART) algorithm.

Usage

riAFTBART_fit(
  M.burnin,
  M.keep,
  M.thin = 1,
  status,
  y.train,
  x.train,
  trt.train,
  x.test,
  trt.test,
  cluster.id,
  verbose = FALSE,
  SA = FALSE,
  prior_c_function_used = NULL,
  gps = NULL
)

Arguments

M.burnin

A numeric value indicating the number of MCMC iterations to be treated as burn in.

M.keep

A numeric value indicating the number of MCMC posterior draws after burn in.

M.thin

A numeric value indicating the thinning parameter.

status

A vector of event indicators: status = 1 indicates that the event was observed while status = 0 indicates the observation was right-censored.

y.train

A vector of follow-up times.

x.train

A dataframe or matrix, including all the covariates but not treatments for training data, with rows corresponding to observations and columns to variables.

trt.train

A numeric vector representing the treatment groups for the training data. If there's no treatment indicator, then set to NULL.

x.test

A dataframe or matrix, including all the covariates but not treatments for testing data, with rows corresponding to observations and columns to variables.

trt.test

A numeric vector representing the treatment groups for the testing data. If there's no treatment indicator, then set to NULL.

cluster.id

A vector of integers representing the clustering id. The cluster id should be an integer and start from 1.

verbose

A logical indicating whether to show the progress bar. The default is FALSE

SA

A logical indicating whether to conduct sensitivity analysis. The default is FALSE.

prior_c_function_used

Prior confounding functions used for SA, which is inherited from the sa function. The default is NULL.

gps

Generalized propensity score, which is inherited from the sa function. The default is NULL.

Value

A list with the following elements:

b:

A matrix including samples from the posterior of the random effects.

tree:

A matrix with M.keep rows and nrow(x.train) columns represnting the predicted log survival time for x.train.

tree.pred:

A matrix with M.keep rows and nrow(x.test) columns represnting the predicted log survival time for x.test.

tau:

A vector representing the posterior samples of tau, the standard deviation of the random effects.

sigma:

A vector representing the posterior samples of sigma, the residual/error standard deviation.

vip:

A matrix with M.keep rows and ncol(x.train) columns represnting the variable inclusion proportions for each variable.

Examples

library(riAFTBART)
set.seed(20181223)
n = 5       # number of clusters
k = 50      # cluster size
N = n*k     # total sample size
cluster.id = rep(1:n, each=k)
tau.error = 0.8
b = stats::rnorm(n, 0, tau.error)
alpha = 2
beta1 = 1
beta2 = -1
sig.error = 0.5
censoring.rate = 0.02
x1 = stats::rnorm(N,0.5,1)
x2 = stats::rnorm(N,1.5,0.5)
trt.train = sample(c(1,2,3), N, prob = c(0.4,0.3,0.2), replace = TRUE)
trt.test = sample(c(1,2,3), N, prob = c(0.3,0.4,0.2), replace = TRUE)
error = stats::rnorm(N,0,sig.error)
logtime = alpha + beta1*x1 + beta2*x2 + b[cluster.id] + error
y = exp(logtime)
C = rexp(N, rate=censoring.rate) # censoring times
Y = pmin(y,C)
status = as.numeric(y<=C)
res <- riAFTBART_fit(M.burnin = 10, M.keep = 10, M.thin = 1, status = status,
                      y.train = Y, trt.train = trt.train, trt.test = trt.test,
                      x.train = cbind(x1,x2),
                      x.test = cbind(x1,x2),
                      cluster.id = cluster.id)