plotBrier: Time-dependent Brier scores

In BayesSurvive: Bayesian Survival Models for High-Dimensional Data

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 = 50
)
# predict survival probabilities of the train data
plotBrier(fit, survObj.new = dataset)

BayesSurvive documentation built on April 3, 2025, 5:58 p.m.