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

## -----------------------------------------------------------------------------
library(gst)
library(dplyr)

# load data from the `multcomp` package
dat <- multcomp::mtept
head(dat)

## -----------------------------------------------------------------------------
# correlation between endpoints
dat |> 
  select(-treatment) |> 
  cor()

## -----------------------------------------------------------------------------
# reverse E4
dat <- dat |>
  mutate(
    E4 = -E4
  )

# correlation between endpoints
dat |> 
  select(-treatment) |> 
  cor()

## -----------------------------------------------------------------------------
results <- dat |>
  obrien(
    endpoint_vars = c("E1", "E2", "E3", "E4"),
    treatment_vars = "treatment",
    method = "ols"
  )
print(data.frame(results[-5]))

## -----------------------------------------------------------------------------
results <- dat |>
  obrien(
    endpoint_vars = c("E1", "E2", "E3", "E4"),
    treatment_vars = "treatment",
    method = "gls"
  )
print(data.frame(results[-5]))

## -----------------------------------------------------------------------------
results <- dat |>
  obrien(
    endpoint_vars = c("E1", "E2", "E3", "E4"),
    treatment_vars = "treatment",
    method = "ranksum" 
  )
print(data.frame(results[-5]))

## -----------------------------------------------------------------------------
# generate all possible combinations of the 4 endpoints (1 = include; 0 = exclude)
opt_mat <- expand.grid(0:1, 0:1, 0:1, 0:1)
opt_mat <- opt_mat |> dplyr::arrange(rowSums(opt_mat))
endpoints <- c("E1", "E2", "E3", "E4")

# run o'brien's test on each combination of endpoints
results <- 
  purrr::map_dfr(
    2:nrow(opt_mat),
    function(i) {
      my_endpoints <- endpoints[ opt_mat[i, ] == 1 ]  # identify endpoints to test
      x <- dat |>
        obrien(
          endpoint_vars = my_endpoints,
          treatment_vars = "treatment",
          method = "ols" 
        )
      x$R <- NULL    # ignore correlation matrix in output (not needed)
      x |>
        dplyr::as_tibble() |>
        dplyr::mutate(
          endpoints = paste(my_endpoints, collapse = ", "),
          method = "ols"
        )
    }
  )

# display results in a nice table (gt is optional; fall back to kable)
results_tbl <- results |>
  dplyr::transmute(
    Endpoints = endpoints,
    DF = df,
    `Test Statistic` = round(statistic, 2),
    `P-value` = round(pvalue, 3)
  )

if (requireNamespace("gt", quietly = TRUE)) {
  results_tbl |>
    gt::gt() |>
    gt::fmt_auto() |>
    gt::tab_header(
      "O'Brien's OLS Test on All Endpoint Combinations"
    )
} else {
  knitr::kable(results_tbl, caption = "O'Brien's OLS Test on All Endpoint Combinations")
}