R/aft_npp.R

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

#' Posterior of normalized power prior (NPP)
#'
#' Sample from the posterior distribution of an accelerated failure time (AFT) model using the
#' normalized power prior (NPP) by Duan et al. (2006) <doi:10.1002/env.752>.
#'
#' Before using this function, users must estimate the logarithm of the normalizing constant across a
#' range of different values for the power prior parameter (\eqn{a_0}), possibly smoothing techniques
#' over a find grid. The power prior parameters (\eqn{a_0}'s) are treated as random with independent
#' beta priors. The initial priors on the regression coefficients are independent normal priors. The
#' current and historical data models are assumed to have a common scale parameter with a half-normal
#' prior.
#'
#' @include data_checks_aft.R
#' @include get_stan_data_aft.R
#' @include aft_npp_lognc.R
#'
#' @export
#'
#' @param formula           a two-sided formula giving the relationship between the response variable and covariates.
#'                          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.
#' @param data.list         a list of `data.frame`s. The first element in the list is the current data, and the rest
#'                          are the historical data sets. For fitting accelerated failure time (AFT) models, all historical
#'                          data sets will be stacked into one historical data set.
#' @param a0.lognc          a vector giving values of the power prior parameter for which the logarithm of the normalizing
#'                          constant has been evaluated.
#' @param lognc             a vector giving the logarithm of the normalizing constant (as estimated by [aft.npp.lognc()] for
#'                          each value of `a0.lognc` using the historical data set.
#' @param dist              a character indicating the distribution of survival times. Currently, `dist` can be one of the
#'                          following values: "weibull", "lognormal", or "loglogistic". Defaults to "weibull".
#' @param 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.
#' @param 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.
#' @param scale.mean        location parameter for the half-normal prior on the scale parameter of the AFT model. Defaults to 0.
#' @param scale.sd          scale parameter for the half-normal prior on the scale parameter of the AFT model. Defaults to 10.
#' @param a0.shape1         first shape parameter for the beta prior on the power prior parameter (\eqn{a_0}). When
#'                          \code{a0.shape1 == 1} and \code{a0.shape2 == 1}, a uniform prior is used.
#' @param a0.shape2         second shape parameter for the beta prior on the power prior parameter (\eqn{a_0}). When
#'                          \code{a0.shape1 == 1} and \code{a0.shape2 == 1}, a uniform prior is used.
#' @param a0.lower          a scalar giving the lower bound for \eqn{a_0}. Defaults to 0.
#' @param a0.upper          a scalar giving the upper bound for \eqn{a_0}. Defaults to 1.
#' @param get.loglik        whether to generate log-likelihood matrix. Defaults to FALSE.
#' @param iter_warmup       number of warmup iterations to run per chain. Defaults to 1000. See the argument `iter_warmup` in
#'                          `sample()` method in cmdstanr package.
#' @param 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.
#' @param chains            number of Markov chains to run. Defaults to 4. See the argument `chains` in `sample()` method in
#'                          cmdstanr package.
#' @param ...               arguments passed to `sample()` method in cmdstanr package (e.g., `seed`, `refresh`, `init`).
#'
#' @return
#'  The function returns an object of class `draws_df` containing posterior samples. The object has two attributes:
#'
#'  \describe{
#'    \item{data}{a list of variables specified in the data block of the Stan program}
#'
#'    \item{model}{a character string indicating the model name}
#'  }
#'
#' @seealso [aft.npp.lognc()]
#'
#' @references
#'  Duan, Y., Ye, K., and Smith, E. P. (2005). Evaluating water quality using power priors to incorporate historical information. Environmetrics, 17(1), 95–106.
#'
#' @examples
#' \donttest{
#'   if(requireNamespace("parallel")){
#'     library(parallel)
#'     ncores    = 2
#'
#'     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)
#'       formula = survival::Surv(failtime, failcens) ~ treatment + sex + cage + node_bin
#'     }
#'
#'     a0 = seq(0, 1, length.out = 11)
#'     if (instantiate::stan_cmdstan_exists()) {
#'       ## call created function
#'       ## wrapper to obtain log normalizing constant in parallel package
#'       logncfun = function(a0, ...){
#'         hdbayes::aft.npp.lognc(
#'           formula = formula, histdata = data_list[[2]], a0 = a0, dist = "weibull",
#'           ...
#'         )
#'       }
#'
#'       cl = makeCluster(ncores)
#'       clusterSetRNGStream(cl, 123)
#'       clusterExport(cl, varlist = c('formula', 'data_list'))
#'       a0.lognc = parLapply(
#'         cl = cl, X = a0, fun = logncfun, iter_warmup = 500,
#'         iter_sampling = 1000, chains = 1, refresh = 0
#'       )
#'       stopCluster(cl)
#'       a0.lognc = data.frame( do.call(rbind, a0.lognc) )
#'
#'       ## sample from normalized power prior
#'       aft.npp(
#'         formula = formula,
#'         data.list = data_list,
#'         a0.lognc = a0.lognc$a0,
#'         lognc = a0.lognc$lognc,
#'         dist = "weibull",
#'         chains = 1, iter_warmup = 500, iter_sampling = 1000,
#'         refresh = 0
#'       )
#'     }
#'   }
#' }
aft.npp = function(
    formula,
    data.list,
    a0.lognc,
    lognc,
    dist              = "weibull",
    beta.mean         = NULL,
    beta.sd           = NULL,
    scale.mean        = NULL,
    scale.sd          = NULL,
    a0.shape1         = 1,
    a0.shape2         = 1,
    a0.lower          = 0,
    a0.upper          = 1,
    get.loglik        = FALSE,
    iter_warmup       = 1000,
    iter_sampling     = 1000,
    chains            = 4,
    ...
) {
  ## get Stan data for NPP
  standat = get.aft.stan.data.npp(
    formula     = formula,
    data.list   = data.list,
    a0.lognc    = a0.lognc,
    lognc       = lognc,
    dist        = dist,
    beta.mean   = beta.mean,
    beta.sd     = beta.sd,
    scale.mean  = scale.mean,
    scale.sd    = scale.sd,
    a0.shape1   = a0.shape1,
    a0.shape2   = a0.shape2,
    a0.lower    = a0.lower,
    a0.upper    = a0.upper,
    get.loglik  = get.loglik
  )

  aft_npp = instantiate::stan_package_model(
    name = "aft_npp",
    package = "hdbayes"
  )

  ## fit model in cmdstanr
  fit = aft_npp$sample(data = standat,
                       iter_warmup = iter_warmup, iter_sampling = iter_sampling, chains = chains,
                       ...)

  ## rename parameters
  p        = standat$p
  X        = standat$X_obs
  oldnames = c(paste0("beta[", 1:p, "]"), "scale", "a0")
  newnames = c(colnames(X), "scale", "a0")

  d        = rename.params(fit = fit, oldnames = oldnames, newnames = newnames)
  ## add data used for the variables specified in the data block of the Stan program as an attribute
  attr(x = d, which = 'data') = standat
  ## add model name as an attribute
  attr(x = d, which = 'model') = "aft_npp"
  return(d)
}