simIC_weib: Simulates interval censored data from regression model with a...

In icenReg: Regression Models for Interval Censored Data

Simulates interval censored data from regression model with a Weibull baseline

Description

Simulates interval censored data from a regression model with a weibull baseline distribution. Used for demonstration

Usage

simIC_weib(
  n = 100,
  b1 = 0.5,
  b2 = -0.5,
  model = "ph",
  shape = 2,
  scale = 2,
  inspections = 2,
  inspectLength = 2.5,
  rndDigits = NULL,
  prob_cen = 1
)

Arguments

n	Number of samples simulated
b1	Value of first regression coefficient
b2	Value of second regression coefficient
model	Type of regression model. Options are 'po' (prop. odds) and 'ph' (Cox PH)
shape	shape parameter of baseline distribution
scale	scale parameter of baseline distribution
inspections	number of inspections times of censoring process
inspectLength	max length of inspection interval
rndDigits	number of digits to which the inspection time is rounded to, creating a discrete inspection time. If rndDigits = NULL, the inspection time is not rounded, resulting in a continuous inspection time
prob_cen	probability event being censored. If event is uncensored, l == u

Details

Exact event times are simulated according to regression model: covariate x1 is distributed rnorm(n) and covariate x2 is distributed 1 - 2 * rbinom(n, 1, 0.5). Event times are then censored with a case II interval censoring mechanism with inspections different inspection times. Time between inspections is distributed as runif(min = 0, max = inspectLength). Note that the user should be careful in simulation studies not to simulate data where nearly all the data is right censored (or more over, all the data with x2 = 1 or -1) or this can result in degenerate solutions!

Author(s)

Clifford Anderson-Bergman

Examples

set.seed(1)
sim_data <- simIC_weib(n = 500, b1 = .3, b2 = -.3, model = 'ph', 
                      shape = 2, scale = 2, inspections = 6, 
                      inspectLength = 1)
#simulates data from a cox-ph with beta weibull distribution.

diag_covar(Surv(l, u, type = 'interval2') ~ x1 + x2, 
           data = sim_data, model = 'po')
diag_covar(Surv(l, u, type = 'interval2') ~ x1 + x2,
           data = sim_data, model = 'ph')

#'ph' fit looks better than 'po'; the difference between the transformed survival
#function looks more constant

icenReg documentation built on May 29, 2024, 5:32 a.m.