predict.BayesSurvive {BayesSurvive}

Predict survival risk

Description

Predict survival probability, (cumulative) hazard or (integrated) Brier scores based on Cox regression models

Usage

## S3 method for class 'BayesSurvive'
predict(
  object,
  survObj.new,
  type = "brier",
  method = "mean",
  times = NULL,
  subgroup = 1,
  verbose = TRUE,
  ...
)

Arguments

object

fitted object obtained with BayesSurvive

survObj.new

a list containing observed data from new subjects with components t, di, x. If type is among c("hazard", "cumhazard", "survival"), only survObj.new$X is needed.

type

option to chose for predicting brier scores with type="brier" or one of type=c("brier", "hazard", "cumhazard", "survival"))

method

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

times

time points at which to evaluate the risks. If NULL (default), the event/censoring times are used. If type="brier", the largest one of the times is used

subgroup

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

verbose

logical value to print IBS of the NULL model and the Bayesian Cox model

...

not used

Value

A list object including seven components with the first compoment as the specified argument type. The other components of the list are "se", "band", "type", "diag", "baseline" and "times", see function riskRegression::predictCox for details

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
predict(fit, survObj.new = dataset)