simulate_mle_performance: Monte Carlo Simulation Framework for MultiFrailty Models

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

Monte Carlo Simulation Framework for MultiFrailty Models

Description

Evaluates frequentist Maximum Likelihood Estimation performance (bias, relative bias, MSE, empirical coverage) across repeated Monte Carlo replications under user-specified baseline, frailty, and censoring schemes.

Usage

simulate_mle_performance(
  n_sim = 50,
  n = 100,
  baseline = "weibull",
  bpar = c(2, 1.5),
  frailty = "gamma",
  fpar = c(0.8),
  beta = 0.5,
  cen_type = "right",
  cen_rate = 0.2
)

Arguments

n_sim	Number of Monte Carlo simulation replicates. Default is 50.
n	Sample size per replicate. Default is 100.
baseline	Character string for baseline hazard ("weibull" or "gw").
bpar	True baseline parameter vector.
frailty	Character string for frailty family ("none", "gamma", "ig", "gl1", or "gl2").
fpar	True frailty parameter vector.
beta	True regression parameter. Default 0.5.
cen_type	Censoring mechanism. Default "right".
cen_rate	Censoring rate. Default 0.2.

Value

A data frame summarizing parameter true values, mean estimates, bias, relative bias, MSE, and coverage.

References

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)
sim_res <- simulate_mle_performance(n_sim = 10, n = 50, baseline = "weibull",
                                    bpar = c(2, 1.5), frailty = "gamma", fpar = c(0.8))
print(sim_res)

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