MTAFT_CV {MTAFT}

MTAFT_CV: Cross-Validation for Multiple Thresholds Accelerated Failure Time Model

Description

This function implements a cross-validation method for the multiple thresholds accelerated failure time (AFT) model using either the "WBS" (Wild Binary Segmentation) or "DP" (Dynamic Programming) algorithm. It determines the optimal number of thresholds by evaluating the cross-validation (CV) values.

Usage

MTAFT_CV(
  Y,
  X,
  delta,
  Tq,
  algorithm,
  dist_min = 50,
  ncps_max = 4,
  wbs_nintervals = 200
)

Arguments

Y

the censored logarithm of the failure time.

X

the design matrix without the intercept.

delta

the censoring indicator.

Tq

the threshold values.

algorithm

the threshold detection algorithm, either "WBS" or "DP".

dist_min

the pre-specified minimal number of observations within each subgroup. Default is 50.

ncps_max

the pre-specified maximum number of thresholds. Default is 4.

wbs_nintervals

the number of random intervals in the WBS algorithm. Default is 200.

Value

A list with the following components:

params

the subgroup-specific slope estimates and variance estimates.

thres

the threshold estimates.

CV_vals

the CV values for all candidate number of thresholds.

Examples

# Generate simulated data with 500 samples and normal error distribution
dataset <- MTAFT_simdata(n = 500, err = "normal")

Y <- dataset[, 1]
delta <- dataset[, 2]
Tq <- dataset[, 3]
X <- dataset[, -c(1:3)]

# Run mAFT_CV with WBS algorithm
maft_cv_result <- MTAFT_CV(Y, X, delta, Tq, algorithm = "WBS")
maft_cv_result$params
maft_cv_result$thres
maft_cv_result$CV_vals