CoxKnotsearch_boot: This function conduct bootstraps for @CoxKnotsearch

In LatencyA: Latency Analysis in Epidemiological Studies Using Weighted Cumulative Exposures

This function conduct bootstraps for @CoxKnotsearch

Description

This function conduct bootstraps for @CoxKnotsearch

Usage

CoxKnotsearch_boot(
  data,
  time_start,
  time_end,
  status,
  exposure,
  knots_number,
  latency,
  adjusted_variable = NULL,
  adjusted_model = NULL,
  lag,
  parallel = FALSE,
  parallel_plan = c("multicore", "multisession"),
  boot_iter = 10,
  id = "id",
  criteria = "AIC"
)

Arguments

data	A data frame containing the data
time_start	start time
time_end	end time
status	survival status. 1 for death and 0 for censored
exposure	Name of the time-varying exposure variable. From the most recent to the furthest in time.
knots_number	A vector of prespecified knot number
latency	prespecified latency
adjusted_variable	A vector of adjusted variables
adjusted_model	A vector of adjusted models
lag	A value of lag time
parallel	A boolean numeric to indicate whether to run the function in parallel
parallel_plan	A string to specify the future plan used when parallel = TRUE. It can be either "multicore" or "multisession". The default is "multicore"
boot_iter	Number of bootstrap iterations
id	A string of the column name that contains the subject ID
criteria	A string to specify the criteria for model selection. It can be either "AIC" or "BIC". The default is "AIC"

Value

A data.frame. The first column is the mean of log HR. The second column is the variance of log HR, the third and fourth columns are the 2.5 and 97.5 percentiles of log HR if parallel = TRUE

Examples

time_start <- "age_start"
time_end <- "age_end"
status <- "failure"
exposure <- paste0("lag", 0:15)
# knots can be set as as a vector of potential number of knots, here we use 3 to save time
knots_number <- c(3)
latency <- 16
adjusted_variable <- "L"
adjusted_model <- "L"
lag <- 2
boot_iter <- 2
id <- "id"
# use a subset of subjects to keep the example fast
ids <- unique(sim_data$id)[1:300]
small_data <- sim_data[sim_data$id %in% ids, ]
result <- CoxKnotsearch_boot(small_data, time_start, time_end, status,
exposure, knots_number = knots_number, latency,
lag = lag, parallel = FALSE, boot_iter = boot_iter, id = id)

# the bootstrap can also be run in parallel
result <- CoxKnotsearch_boot(small_data, time_start, time_end, status,
exposure, knots_number = knots_number, latency,
lag = lag, parallel = TRUE, boot_iter = boot_iter, id = id)

LatencyA documentation built on Oct. 21, 2026, 5:14 p.m.