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exnexSurv

Fast Bayesian EXchangeable–Non-EXchangeable (EXNEX) survival analysis for basket trials via Rcpp Gibbs sampling and data augmentation.

exnexSurv fits an EXNEX hierarchical model for right-censored log-normal survival data. Each basket’s log-location effect is either drawn from a shared exchangeable component (borrowing strength across baskets) or from a basket-specific non-exchangeable prior, selected by a latent indicator. Censored event times are imputed from their truncated-Normal conditional distribution, which makes every full conditional conjugate and the systematic Gibbs scan exact. The sampler is implemented in C++/RcppArmadillo and is roughly 25× faster than a marginalized Stan implementation of the same model on the paper’s simulation study.

Installation

# development version from GitHub
# install.packages("pak")
pak::pak("victorney/exnexSurv")

# or
# remotes::install_github("victorney/exnexSurv")

Quick example

library(exnexSurv)
library(survival)

set.seed(1)
n <- 120
group <- factor(rep(1:3, each = 40))
x1 <- rnorm(n)
eta <- rep(c(1.1, 1.6, 2.0), each = 40) + 0.5 * x1
true_time <- exp(eta + rnorm(n, 0, 0.6))
cens_time <- runif(n, 2, 9)
d <- data.frame(
  time   = pmin(true_time, cens_time),
  event  = as.integer(true_time <= cens_time),
  group  = group,
  x1     = x1
)

fit <- exnex_surv(
  Surv(time, event) ~ group + x1,
  data = d,
  iter = 2000, warmup = 1000, chains = 2, parallel_chains = 2
)

summary(fit)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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