simIC_weib function

Simulates interval censored data from regression model with a Weibull baseline

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

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!

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

Author(s)

Clifford Anderson-Bergman