simIC

The simIC package provides tools for simulating and analyzing interval-censored survival data, including left-, right-, and uncensored observations, using a variety of parametric distributions. It is useful for teaching, model development, and method evaluation in survival analysis.

✨ Features

• Supports commonly used parametric distributions:

  • Weibull
    
  • Exponential
    
  • Log-Normal
    
  • Logistic
    
  • Normal
    
  • Log-Logistic
    
  • Gamma
    
  • Gompertz
    
  • EMV (Extreme Minimum Value / Gumbel)

• Simulates survival data with interval, left, right, and uncensored observations using user-defined visit schedules (start_time, end_time) and an optional tolerance (uncensored_tol) for detecting exact event times.

• Provides two estimation functions:

  • mle_int()
    • Performs direct maximum likelihood estimation
      
    • Automatically detects and handles:
      • Interval-censored: contribution from F(Ri) - F(Li)
        
      • Left-censored: contribution from F(Ri)
        
      • Right-censored: contribution from 1 - F(Li)
        
      • Uncensored: contribution from f(ti)
  • mle_imp()
    • Uses imputation-based likelihood for interval- and uncensored data
      
    • For left- and right-censored, uses the proper likelihood contributions without imputation
      
    • Specifically:
      • Interval-censored: event times imputed from (Li, Ri) using midpoint, random, medians, or survival-based methods
        
      • Left-censored: contribution from F(Ri)
        
      • Right-censored: contribution from 1 - F(Li)
        
      • Uncensored: contribution from f(ti)

📦 Installation

You can install the development version of simIC from GitHub:

```r install.packages(“remotes”) remotes::install_github(“jayarasan/simIC”) library(simIC)

🧪 Simulate Survival Data # Interval-censored data only (no visit window) data <- simIC(n = 100, dist = “weibull”, shape = 1.5, scale = 5, width = 2)

Left-, right-, and uncensored data using a follow-up window and tolerance

data <- simIC(n = 100, dist = “weibull”, shape = 1.5, scale = 5, width = 2, start_time = 0, end_time = 10, uncensored_tol = 0.1)

📈 Model Fitting Examples

Direct MLE for interval-censored data

fit_int <- mle_int(data\(left, data\)right, dist = “weibull”) print(fit_int$estimates)

Imputation-based MLE (midpoint)

fit_imp <- mle_imp(data\(left, data\)right, dist = “weibull”, impute = “midpoint”) print(fit_imp$estimates)

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