hdbayes (version 0.2.0)

aft.logml.stratified.pp: Log marginal likelihood of an accelerated failure time (AFT) model under the stratified power prior (PP)

Description

Uses Markov chain Monte Carlo (MCMC) and bridge sampling to estimate the logarithm of the marginal likelihood of an AFT model under the stratified power prior (PP).

The arguments related to MCMC sampling are utilized to draw samples from the stratified power prior (PP). These samples are then used to compute the logarithm of the normalizing constant of the stratified PP using only historical data sets.

Usage

aft.logml.stratified.pp(
  post.samples,
  bridge.args = NULL,
  iter_warmup = 1000,
  iter_sampling = 1000,
  chains = 4,
  ...
)

Value

The function returns a list with the following objects

model

"aft_stratified_pp"

logml

the estimated logarithm of the marginal likelihood

bs

an object of class bridge or bridge_list containing the output from using bridgesampling::bridge_sampler() to compute the logarithm of the normalizing constant of the stratified power prior (PP) using all data sets

bs.hist

an object of class bridge or bridge_list containing the output from using bridgesampling::bridge_sampler() to compute the logarithm of the normalizing constant of the stratified PP using historical data sets

min_ess_bulk

the minimum estimated bulk effective sample size of the MCMC sampling

max_Rhat

the maximum Rhat

Arguments

post.samples

output from aft.stratified.pp() giving posterior samples of an AFT model under the stratified power prior (PP), with an attribute called 'data' which includes the list of variables specified in the data block of the Stan program.

bridge.args

a list giving arguments (other than samples, log_posterior, data, lb, and ub) to pass onto bridgesampling::bridge_sampler().

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).

References

Wang, C., Li, H., Chen, W.-C., Lu, N., Tiwari, R., Xu, Y., & Yue, L. Q. (2019). Propensity score-integrated power prior approach for incorporating real-world evidence in single-arm clinical studies. Journal of Biopharmaceutical Statistics, 29(5), 731–748.

Gronau, Q. F., Singmann, H., and Wagenmakers, E.-J. (2020). bridgesampling: An r package for estimating normalizing constants. Journal of Statistical Software, 92(10).

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
    data_list = list(currdata = E1690, histdata = E1684)
    strata_list = list(rep(1:2, each = 25), rep(1:2, each = 50))
    # Alternatively, we can determine the strata based on propensity scores
    # using the psrwe package, which is available on GitHub
    d.stratified.pp = aft.stratified.pp(
      formula = survival::Surv(failtime, failcens) ~ treatment,
      data.list = data_list,
      strata.list = strata_list,
      a0.strata = c(0.3, 0.5),
      dist = "weibull",
      chains = 1, iter_warmup = 500, iter_sampling = 1000
    )
    aft.logml.stratified.pp(
      post.samples = d.stratified.pp,
      bridge.args = list(silent = TRUE),
      chains = 1, iter_warmup = 1000, iter_sampling = 2000
    )
  }
}