flassomsm (version 0.1.0)

prederr: prederr

Description

Evaluates the predictive performance of the multi-state model

Usage

prederr(
  msdata,
  X,
  beta_est,
  times,
  state_of_interest,
  trans_matrix,
  test_fraction = 0.2,
  quick = FALSE,
  verbose = FALSE
)

Value

Gives a list of objects like brier score at specified time points, Integrated Brier score,predicted probabilities, their true states and the time points

Arguments

msdata

is a multi-state model in extended form having columns Tstart,Tstop,trans (covariates expanded transition wise)

X

covariate matrix of the original covariates before expanding, (for example if the dataset initially contains 4 covariates then the matrix has to be formed with the 4 covariates only and not their expanded version )

beta_est

estimated beta coefficients from the fitted model

times

time points at which prediction error is to be calculated

state_of_interest

the target state for which prediction accuracy is evaluated

trans_matrix

transition matrix initially defined for multi-state model

test_fraction

fraction of subjects randomly assigned to test set

quick

specify whether the model will run complete code or quick mode

verbose

Logical indicating whether to print progress messages

Author

Atanu Bhattacharjee,Gajendra Kumar Vishwakarma,Abhipsa Tripathy

Details

This function is designed to evaluate the predictive performance of a multi-state survival model using Brier score using subject specific covatiates and thier estimated coefficients from a penalized regression model. It also incorporates baseline hazards for each transtion from a Cox model using msfit() for more accurate prediction and also computes the predicted state probabilities with probtrans().

Examples

##
set.seed(123)
data(msdata_3state)
tmat_3state <- mstate::transMat(x = list(c(2, 3), c(3), c()),
                       names = c("State1", "State2", "State3"))
beta_est1 <- c(0.13, -0.16, -0.12, -0.20, -0.15, -0.55)
prederr(msdata = msdata_3state, X = msdata_3state[, 9:10], beta_est = beta_est1,
times = seq(0.5, 1.5, length.out = 5),state_of_interest = 2,
trans_matrix = tmat_3state,test_fraction = 0.2,quick = TRUE,verbose = TRUE)

# \donttest{
data(msdata_4state)
set.seed(123)
sub_msdata_4state <- msdata_4state[msdata_4state$id %in% sample(unique(msdata_4state$id), 10), ]
tmat_4state <- mstate::transMat(x = list(c(2, 3, 4,5), c(3, 4, 5), c(4,5), c(5),c()),
                               names = c("Tx", "Lrc","Fp", "Dp", "srv"))
beta_est1 <- as.numeric (c(0.13,-0.16,-0.12,-0.20,-0.15,-0.55,-0.35,-0.28,-0.34,-0.12))
times1 <- seq(0.5, 1.5, length.out = 5)
prederr(msdata = sub_msdata_4state, X = sub_msdata_4state[,9],
       beta_est = beta_est1,times = times1,state_of_interest = 2,
       trans_matrix = tmat_4state,test_fraction = 0.2,quick = TRUE,verbose = TRUE)
# }
##