Introduction to MultiFrailty: Shared Frailty Regression Models

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

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(MultiFrailty)
library(survival)

Overview

The MultiFrailty package provides tools for fitting and analyzing shared frailty survival regression models. Shared frailty models incorporate unobserved individual heterogeneity into proportional hazard settings.

Supported Frailty & Baseline Distributions

MultiFrailty supports 10 model combinations across 5 frailty families and 2 baseline hazard functions:

• Frailty Distributions: none, gamma, ig (Inverse Gaussian), gl1 (Generalized Lindley Type 1), gl2 (Generalized Lindley Type 2).
• Baseline Hazard Distributions: weibull (2-parameter Weibull) and gw (3-parameter Generalized Weibull).

Basic Usage Example

library(MultiFrailty)
library(survival)

# Generate synthetic survival data under Gamma frailty with Weibull baseline
set.seed(123)
dat <- r_frailty(n = 80, baseline = "weibull", bpar = c(2.0, 1.5),
                 frailty = "gamma", fpar = c(0.8),
                 x = matrix(rnorm(80), ncol = 1), beta = 0.5)

# Fit model using formula interface
fit <- multifrailty(Surv(time, status) ~ X1, data = dat,
                    baseline = "weibull", frailty = "gamma")

# Summarize fit
summary(fit)

# Predict survival probabilities
pred_surv <- predict_frailty(fit, type = "survival", newtime = c(1, 2, 3))

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