curepwe.leap: Posterior of latent exchangeability prior (LEAP)

In hdbayes: Bayesian Analysis of Generalized Linear Models with Historical Data

Posterior of latent exchangeability prior (LEAP)

Description

Sample from the posterior distribution of a mixture cure rate model (referred to as the CurePWE model) using the latent exchangeability prior (LEAP) by Alt et al. (2024) doi:10.1093/biomtc/ujae083. The CurePWE model assumes that a fraction \pi of the population is "cured", while the remaining 1 - \pi are susceptible to the event of interest. The survival function for the entire population is given by:

S_{\text{pop}}(t) = \pi + (1 - \pi) S(t),

where S(t) represents the survival function of the non-cured individuals. We model S(t) using a piecewise exponential (PWE) model (i.e., a proportional hazards model with a piecewise constant baseline hazard). Covariates are incorporated through the PWE model.

Usage

curepwe.leap(
  formula,
  data.list,
  breaks,
  K = 2,
  prob.conc = NULL,
  gamma.lower = 0,
  gamma.upper = 1,
  beta.mean = NULL,
  beta.sd = NULL,
  base.hazard.mean = NULL,
  base.hazard.sd = NULL,
  logit.pcured.mean = NULL,
  logit.pcured.sd = NULL,
  get.loglik = FALSE,
  iter_warmup = 1000,
  iter_sampling = 1000,
  chains = 4,
  ...
)

Arguments

formula	a two-sided formula giving the relationship between the response variable and covariates in the PWE model. The response is a survival object as returned by the survival::Surv(time, event) function, where event is a binary indicator for event (0 = no event, 1 = event has occurred). The type of censoring is assumed to be right-censoring.
data.list	a list of data.frames. The first element in the list is the current data, and the rest are the historical data sets. For fitting CurePWE models, all historical data sets will be stacked into one historical data set.
breaks	a numeric vector specifying the time points that define the boundaries of the piecewise intervals. The values should be in ascending order, with the final value being greater than or equal to the maximum observed time.
K	the desired number of classes to identify. Defaults to 2.
prob.conc	a scalar or a vector of length K giving the concentration parameters for Dirichlet prior. If length == 2, a Beta(prob.conc[1], prob.conc[2]) prior is used. If a scalar is provided, prob.conc will be a vector of repeated elements of the given scalar. Defaults to a vector of 1s.
gamma.lower	a scalar giving the lower bound for probability of subjects in historical data being exchangeable with subjects in current data. Defaults to 0.
gamma.upper	a scalar giving the upper bound for probability of subjects in historical data being exchangeable with subjects in current data. Defaults to 1.
beta.mean	a scalar or a vector whose dimension is equal to the number of regression coefficients giving the mean parameters for the initial prior on regression coefficients. If a scalar is provided, beta.mean will be a vector of repeated elements of the given scalar. Defaults to a vector of 0s.
beta.sd	a scalar or a vector whose dimension is equal to the number of regression coefficients giving the sd parameters for the initial prior on regression coefficients. If a scalar is provided, same as for beta.mean. Defaults to a vector of 10s.
base.hazard.mean	a scalar or a vector whose dimension is equal to the number of intervals giving the location parameters for the half-normal priors on the baseline hazards of the PWE model. If a scalar is provided, same as for beta.mean. Defaults to 0.
base.hazard.sd	a scalar or a vector whose dimension is equal to the number of intervals giving the scale parameters for the half-normal priors on the baseline hazards of the PWE model. If a scalar is provided, same as for beta.mean. Defaults to 10.
logit.pcured.mean	mean parameter for the normal prior on the logit of the cure fraction \pi. Defaults to 0.
logit.pcured.sd	sd parameter for the normal prior on the logit of the cure fraction \pi. Defaults to 3.
get.loglik	whether to generate log-likelihood matrix. Defaults to FALSE.
iter_warmup	number of warmup iterations to run per chain. Defaults to 1000. See the argument iter_warmup in sample() method in cmdstanr package.
iter_sampling	number of post-warmup iterations to run per chain. Defaults to 1000. See the argument iter_sampling in sample() method in cmdstanr package.
chains	number of Markov chains to run. Defaults to 4. See the argument chains in sample() method in cmdstanr package.
...	arguments passed to sample() method in cmdstanr package (e.g., seed, refresh, init).

Details

The latent exchangeability prior (LEAP) discounts the historical data by identifying the most relevant individuals from the historical data. It is equivalent to a prior induced by the posterior of a finite mixture model for the historical data set.

Value

The function returns an object of class draws_df containing posterior samples. The object has two attributes:

data

a list of variables specified in the data block of the Stan program

model

a character string indicating the model name

References

Alt, E. M., Chang, X., Jiang, X., Liu, Q., Mo, M., Xia, H. M., and Ibrahim, J. G. (2024). LEAP: The latent exchangeability prior for borrowing information from historical data. Biometrics, 80(3).

Examples

if (instantiate::stan_cmdstan_exists()) {
  if(requireNamespace("survival")){
    library(survival)
    data(E1684)
    data(E1690)
    ## take subset for speed purposes
    E1684 = E1684[1:100, ]
    E1690 = E1690[1:50, ]
    ## replace 0 failure times with 0.50 days
    E1684$failtime[E1684$failtime == 0] = 0.50/365.25
    E1690$failtime[E1690$failtime == 0] = 0.50/365.25
    E1684$cage = as.numeric(scale(E1684$age))
    E1690$cage = as.numeric(scale(E1690$age))
    data_list = list(currdata = E1690, histdata = E1684)
    nbreaks = 3
    probs   = 1:nbreaks / nbreaks
    breaks  = as.numeric(
      quantile(E1690[E1690$failcens==1, ]$failtime, probs = probs)
    )
    breaks  = c(0, breaks)
    breaks[length(breaks)] = max(10000, 1000 * breaks[length(breaks)])
    curepwe.leap(
      formula = survival::Surv(failtime, failcens) ~ treatment + sex + cage + node_bin,
      data.list = data_list,
      breaks = breaks,
      K = 2,
      logit.pcured.mean = 0, logit.pcured.sd = 3,
      chains = 1, iter_warmup = 500, iter_sampling = 1000
    )
  }
}

hdbayes documentation built on Nov. 21, 2025, 1:07 a.m.