CoxKnotsearch: This function implments GridSearch of natural cubic splines...

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

This function implments GridSearch of natural cubic splines in Cox regression for latency analysis This is GreadSearching by knots

Description

This function implments GridSearch of natural cubic splines in Cox regression for latency analysis This is GreadSearching by knots

Usage

CoxKnotsearch(
  data,
  time_start,
  time_end,
  status,
  exposure,
  knots_number,
  latency,
  adjusted_variable = NULL,
  adjusted_model = NULL,
  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 potential number of knots
latency	pre-specified latency
adjusted_variable	A vector of adjusted variables
adjusted_model	A vector of adjusted models
criteria	A string to specify the criteria for model selection. It can be either "AIC" or "BIC". The default is "AIC"

Value

A list. The first element is the best knots with the lowest AIC. The second element is a Cox model

Examples

time_start <- "age_start"
time_end <- "age_end"
status <- "failure"
exposure <- paste0("lag", 0:19)
# 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 <- 20
fit <- CoxKnotsearch(sim_data, time_start, time_end, status, exposure, knots_number, latency)

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