Bootstrap CI for adjusted CIF

Use input data, time, status,grouping variables, adjusted covariates, events of interests, whether to use stratified model, and defining reference group as inputs

boot_ci_adj_km(
  boot_n = 100,
  ci_cut = c(0.025, 0.975),
  data,
  time,
  status,
  group,
  covlist,
  stratified_cox,
  reference_group
)

Arguments

boot_n

bootstrap sample size

ci_cut

default c(0.025, 0.975) bootstrap 95% CI

data

the input dataset

time

column name of time variable

status

column name of event status

group

grouping variable

covlist

list of covariates that should be included in the model

stratified_cox

"Yes" refers to use stratified model, "No" refers to use coxph regression

reference_group

NULL- unstratified coxph when stratified = No; "G&B"- G&B when stratified = Yes; Otherwise, Storer's approach will be performed when using a self-defined reference

Value

Output is a dataframe with average number of adjusted survival probabilities, as well as 2.5% and 97.5% percentiles.

Examples

# Data preparation
library(KMsurv)
data(bmt)
bmt$arm <- bmt$group
bmt$arm = factor(as.character(bmt$arm), levels = c("2", "1", "3"))
bmt$z3 = as.character(bmt$z3)
bmt$t2 = bmt$t2 * 12/365.25

# Unstratified cox
result1_1 = boot_ci_adj_km(boot_n = 100, ci_cut = c(0.025, 0.975), data = bmt, time = "t2",
status = "d3", group = "arm", covlist = c("z1", "z3"), stratified_cox = "No",
reference_group = NULL)

# Stratified Cox: Gail and Byar's method
result1_2 = boot_ci_adj_km(boot_n = 100, ci_cut = c(0.025, 0.975), data = bmt, time = "t2",
status = "d3", group = "arm", covlist = c("z1", "z3"), stratified_cox = "Yes",
reference_group = "G&B")

# Stratified Cox: Storer's approach
result1_3 = boot_ci_adj_km(boot_n = 100, ci_cut = c(0.025, 0.975), data = bmt, time = "t2",
status = "d3", group = "arm", covlist = c("z1", "z3"), stratified_cox = "Yes",
reference_group = "arm:2")