## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(AHTauDesign)

## Precomputed full-setting results, generated by
## data-raw/precomputed.R (not run at build time).
prec_file <- "precomputed_results.rds"
if (!file.exists(prec_file)) {
  prec_file <- file.path("vignettes", prec_file)
}
pre <- readRDS(prec_file)

## ----toy-twostage-------------------------------------------------------------
set.seed(202608)
toy_result <- twostage_tau(
  N = 60, A = 24, F_followup = 6,
  tau_clin = 24,
  tau_grid = seq(6, 24, by = 6),
  effect_model = list(type = "ph", lambda0 = -log(0.5) / 24, HR = 0.5),
  delta = 12, epsilon = 0.05,
  n_sim = 10, n_boot = 10)

toy_result


## -----------------------------------------------------------------------------
effect_model <- list(type = "ph", lambda0 = -log(0.5) / 24, HR = 0.5)

## ----stage1-code, eval = FALSE------------------------------------------------
# set.seed(202608)
# assess_tau(
#   N = 60, A = 24, F_followup = 6,
#   tau = 24,
#   effect_model = effect_model,
#   n_sim = 100, n_boot = 100)
# 

## ----stage1-show, echo = FALSE------------------------------------------------
pre$assess_base

## ----approach1-code, eval = FALSE---------------------------------------------
# set.seed(202608)
# assess_tau(N = 60, A = 24, F_followup = 24, tau = 24,
#            effect_model = effect_model,
#            n_sim = 100, n_boot = 100)
# 

## ----approach1-show, echo = FALSE---------------------------------------------
pre$assess_long_fu


## ----approach2-code, eval = FALSE---------------------------------------------
# set.seed(202608)
# assess_tau(N = 60, A = 24, F_followup = 6, tau = 12,
#            effect_model = effect_model,
#            n_sim = 100, n_boot = 100)
# 

## ----approach2-show, echo = FALSE---------------------------------------------
pre$assess_tau12


## ----approach3-code, eval = FALSE---------------------------------------------
# set.seed(202608)
# assess_tau(N = 200, A = 24, F_followup = 6, tau = 24,
#            effect_model = effect_model,
#            n_sim = 100, n_boot = 100)
# 

## ----approach3-show, echo = FALSE---------------------------------------------
pre$assess_n200


## -----------------------------------------------------------------------------
default_fail_thresholds()


## ----relaxed-code, eval = FALSE-----------------------------------------------
# set.seed(202608)
# assess_tau(N = 60, A = 24, F_followup = 6, tau = 24,
#            effect_model = effect_model,
#            fail_thresholds = list(total_risk = 5, arm_risk = 2),
#            n_sim = 100, n_boot = 100)
# 

## ----relaxed-show, echo = FALSE-----------------------------------------------
pre$assess_relaxed


## ----optimize-code, eval = FALSE----------------------------------------------
# set.seed(202608)
# optimize_tau(
#   N = 60, A = 24, F_followup = 6,
#   tau_clin = 24,
#   tau_grid = seq(6, 36, by = 3),
#   effect_model = effect_model,
#   delta = 12, epsilon = 0.05,
#   n_sim = 100, n_boot = 100)
# 

## ----optimize-show, echo = FALSE----------------------------------------------
pre$optimization


## ----twostage-code, eval = FALSE----------------------------------------------
# set.seed(202608)
# result <- twostage_tau(
#   N = 60, A = 24, F_followup = 6,
#   tau_clin = 24,
#   tau_grid = seq(6, 36, by = 3),
#   effect_model = effect_model,
#   delta = 12, epsilon = 0.05,
#   n_sim = 100, n_boot = 100)
# 
# summary(result)
# 

## ----twostage-show, echo = FALSE----------------------------------------------
result <- pre$twostage
summary(result)


## ----fig.width = 7, fig.height = 4.5------------------------------------------
plot(result)