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

## ----setup--------------------------------------------------------------------
library(LAGOtrials)

## -----------------------------------------------------------------------------
bb_data <- BB_data
head(bb_data[, c("coaching_updt", "launch_duration", "pp3_oxytocin_mother")])

## -----------------------------------------------------------------------------
result <- lago_optimization(
  data = bb_data,
  outcome_name = "pp3_oxytocin_mother",
  outcome_type = "binary",
  intervention_components = c("coaching_updt", "launch_duration"),
  intervention_lower_bounds = c(1, 1),
  intervention_upper_bounds = c(40, 5),
  center_characteristics = "birth_volume_100",
  center_characteristics_optimization_values = 1.75,
  cost_list_of_vectors = list(c(0, 1700), c(0, 8000)),
  outcome_goal = 0.85,
  outcome_goal_intention = "maximize",
  confidence_set_grid_step_size = c(1, 0.5),
  quiet = TRUE
)

## -----------------------------------------------------------------------------
result

## -----------------------------------------------------------------------------
summary(result)

## -----------------------------------------------------------------------------
result$rec_int
result$rec_int_cost

## ----fig.width = 6, fig.height = 4--------------------------------------------
plot(result)

## -----------------------------------------------------------------------------
bb_data$group <- ifelse(bb_data$pre_post == 0, "control", "treatment")

power_result <- lago_optimization(
  data = bb_data,
  outcome_name = "pp3_oxytocin_mother",
  outcome_type = "binary",
  intervention_components = c("coaching_updt", "launch_duration"),
  intervention_lower_bounds = c(1, 1),
  intervention_upper_bounds = c(40, 5),
  center_characteristics = "birth_volume_100",
  center_characteristics_optimization_values = 1.75,
  cost_list_of_vectors = list(c(0, 1700), c(0, 8000)),
  power_goal = 0.8,
  num_centers_in_next_stage = 10,
  patients_per_center_in_next_stage = 30,
  include_confidence_set = FALSE,
  quiet = TRUE
)

power_result$est_outcome_goal

## -----------------------------------------------------------------------------
sens <- lago_sensitivity(
  result,
  parameter = "outcome_goal",
  values = c(0.75, 0.80, 0.85, 0.90)
)

sens

## ----fig.width = 6, fig.height = 4--------------------------------------------
plot(sens)