CoxNCSpline: This function implments natural cubic splines in Cox...

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

This function implments natural cubic splines in Cox regression for latency analysis

Description

This function implments natural cubic splines in Cox regression for latency analysis

Usage

CoxNCSpline(
  data,
  time_start,
  time_end,
  status,
  exposure,
  knots_number,
  latency,
  adjusted_variable = NULL,
  adjusted_model = NULL,
  id = "id"
)

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	Pre-specified number of knots
latency	prespecified latency
adjusted_variable	A vector of adjusted variables
adjusted_model	A vector of adjusted models
id	A string to specify the name of the id column

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_number <- 3
latency <- 20
fit <- CoxNCSpline(sim_data, time_start, time_end, status, exposure, knots_number, latency)

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