plotBrier {BayesSurvive}

Time-dependent Brier scores

Description

Predict time-dependent Brier scores based on Cox regression models

Usage

plotBrier(
  object,
  survObj.new = NULL,
  method = "mean",
  times = NULL,
  subgroup = 1
)

Arguments

object

fitted object obtained with BayesSurvive

survObj.new

a list containing observed data from new subjects with components t, di, X

method

option to use the posterior mean ("mean") of coefficients for prediction or Bayesian model averaging ("BMA") for prediction

times

maximum time point to evaluate the prediction

subgroup

index of the subgroup in survObj.new for prediction. Default value is 1

Value

A ggplot2::ggplot object. See ?ggplot2::ggplot for more details of the object.

Examples

library("BayesSurvive")
set.seed(123)

# Load the example dataset
data("simData", package = "BayesSurvive")

dataset <- list(
  "X" = simData[[1]]$X,
  "t" = simData[[1]]$time,
  "di" = simData[[1]]$status
)

# Initial value: null model without covariates
initial <- list("gamma.ini" = rep(0, ncol(dataset$X)))
# Hyperparameters
hyperparPooled <- list(
  "c0"     = 2, # prior of baseline hazard
  "tau"    = 0.0375, # sd for coefficient prior
  "cb"     = 20, # sd for coefficient prior
  "pi.ga"  = 0.02, # prior variable selection probability for standard Cox models
  "a"      = -4, # hyperparameter in MRF prior
  "b"      = 0.1, # hyperparameter in MRF prior
  "G"      = simData$G # hyperparameter in MRF prior
)


# run Bayesian Cox with graph-structured priors
fit <- BayesSurvive(
  survObj = dataset, hyperpar = hyperparPooled,
  initial = initial, nIter = 100
)
# predict survival probabilities of the train data
plotBrier(fit, survObj.new = dataset)