inst/doc/A600-BEBOP.R

In trialr: Clinical Trial Designs in 'rstan'

## ---- echo=FALSE--------------------------------------------------------------
knitr::kable(data.frame(
  i = 1:6, 
  Pretreated = c(F, F, F, T, T, T),
  PDL1 = c('Low', 'Medium', 'High', 'Low', 'Medium', 'High'),
  x1 = rep(c(0, 1), each = 3),
  x2 = c(1, 0, 0, 1, 0, 0),
  x3 = c(0, 1, 0, 0, 1, 0)
))

## ----message=FALSE------------------------------------------------------------
library(trialr)
peps2_sc <- function() peps2_get_data(num_patients = 60,
                                      prob_eff = c(0.167, 0.192, 0.5, 0.091, 0.156, 0.439),
                                      prob_tox = rep(0.1, 6),
                                      eff_tox_or = rep(1, 6))

set.seed(123)
dat <- peps2_sc()

## -----------------------------------------------------------------------------
c(dat$alpha_mean, dat$alpha_sd)

## -----------------------------------------------------------------------------
knitr::kable(
  head(with(dat, data.frame(eff, tox, x1, x2, x3)), 10)
)

## ---- results = "hide"--------------------------------------------------------
fit <- stan_peps2(dat$eff, dat$tox, dat$cohorts)

## ---- message = FALSE, fig.width = 7, fig.height = 7, fig.cap = "Posterior Prob(Efficacy) in the six PePS2 cohorts"----
rstan::plot(fit, pars = 'prob_eff')

## ---- eval=TRUE---------------------------------------------------------------
decision <- peps2_process(fit)
knitr::kable(
  with(decision, data.frame(ProbEff, ProbAccEff, ProbTox, ProbAccTox, Accept)), 
  digits = 3
)

## ---- eval=FALSE--------------------------------------------------------------
#  set.seed(123)
#  run_sims <- function(num_sims = 10, sample_data_func = peps2_sc,
#                       summarise_func = peps2_process, ...) {
#    sims <- list()
#    for(i in 1:num_sims) {
#      print(i)
#      dat <- sample_data_func()
#      fit <- stan_peps2(dat$eff, dat$tox, dat$cohorts, ...)
#      sim <- summarise_func(fit)
#      sims[[i]] <- sim
#    }
#    return(sims)
#  }
#  
#  sims <- run_sims(num_sims = 10, sample_data_func = peps2_sc,
#                   summarise_func = peps2_process)

## ---- eval=FALSE--------------------------------------------------------------
#  apply(sapply(sims, function(x) x$Accept), 1, mean)