r_frailty: Random Data Generation under Eight Censoring Schemes

In MultiFrailty: Shared Frailty Regression Models with Inverse Gaussian, Generalized Lindley, and Gamma Frailty Distributions

Random Data Generation under Eight Censoring Schemes

Description

Generates survival times and censoring indicators for shared frailty regression models across all 10 baseline-frailty combinations under eight distinct censoring mechanisms.

Usage

r_frailty(
  n,
  baseline = c("weibull", "gw"),
  bpar,
  frailty = c("none", "gamma", "ig", "gl1", "gl2"),
  fpar = numeric(0),
  x = matrix(nrow = n, ncol = 0),
  beta = numeric(0),
  cen_type = c("none", "right", "left", "interval", "type1", "type2", "progressive",
    "progressive_type1"),
  cen_rate = 0.2,
  left_threshold = NULL,
  int_width = NULL,
  cen_time = NULL,
  r_failures = NULL,
  prog_scheme = NULL,
  prog_times = NULL
)

Arguments

n	Number of observations to generate.
baseline	Baseline hazard distribution ("weibull" or "gw").
bpar	Baseline parameter vector.
frailty	Frailty distribution ("none", "gamma", "ig", "gl1", or "gl2").
fpar	Frailty parameter vector.
x	Matrix of covariates (n x p). Default is 0-column matrix.
beta	Regression coefficient vector matching columns of x.
cen_type	Censoring mechanism: "none", "right", "left", "interval", "type1", "type2", "progressive", or "progressive_type1".
cen_rate	Exponential rate for right-censoring time generation. Default is 0.2.
left_threshold	Threshold for left censoring. Default is 20th percentile.
int_width	Width of censoring window for interval censoring. Default is 20% of mean time.
cen_time	Fixed cutoff time for Type-I censoring. Default is 70th percentile.
r_failures	Target number of failures for Type-II censoring. Default is floor(0.7 * n).
prog_scheme	Vector of progressive removal counts for progressive censoring.
prog_times	Inspection time points for progressive Type-I censoring.

Value

A data frame containing generated time, time2 (for interval), status, and covariates.

References

Hougaard, P. (1984). Life table methods for heterogeneous populations: distributions of frailties. Biometrika, 71(1), 75-83.

Pandey, A., Hanagal, D. D., & Tyagi, S. (2022). Shared Frailty Models Based on Cancer Data. International Journal of Statistics and Reliability Engineering, 9(3), 461-474.

Pandey, A., & Tyagi, S. (2021). Comparison of Multiplicative Frailty Models Under Weibull Baseline Distribution. Lobachevskii Journal of Mathematics, 42(13), 3184-3195.

Examples

set.seed(123)
dat <- r_frailty(n = 100, baseline = "weibull", bpar = c(2, 1.5),
                 frailty = "gl1", fpar = c(1.2, 0.5),
                 cen_type = "right", cen_rate = 0.1)
head(dat)

MultiFrailty documentation built on Aug. 8, 2026, 1:07 a.m.