TOLERANCE <- 1e-2


test_that("Table #2: POLARIX ", {
  # OS monitoring guideline retrospectively applied to Motivating Example 1 with δnull = 1.3, δalt = 0.80, γFA = 0.025 and βPA = 0.10.
  res <- bounds(
    events = c(60, 89, 110, 131, 178),
    power_int = 0.9,
    falsepos = 0.025,
    hr_null = 1.3,
    hr_alt = 0.8
  )

  # check for errors in the provided results.
  expect_equal(
    as.numeric(res$summary$'OS HR threshold for positivity'),
    # OS HR threshold for positivity
    c(1.114,  1.050, 1.021, 1.001,  0.969),
    tolerance = TOLERANCE
  )
  expect_equal(
    as.numeric(res$summary$'One-sided false positive error rate'),
    # One-sided false positive error rate
    c(0.275, 0.157, 0.103, 0.067, 0.025),
    tolerance = TOLERANCE
  )
  expect_equal(as.numeric(res$summary$'Level of 2-sided CI needed to rule out delta null'),
               # Level of 2-sided CI needed to rule out
               c(45, 69, 79, 87, 95),
               tolerance = 1e-1)
  expect_equal(as.numeric(res$summary$'Probability of meeting positivity threshold under delta alt'),
               # Probability of meeting positivity threshold under δalt
               c(0.90, 0.90, 0.90, 0.90, 0.90),
               tolerance = TOLERANCE)
})

test_that("Table#3: Intermediate events", {
  # OS monitoring guideline with δnull = 1.3, δalt = 0.70, γFA = 0.10 and βPA = 0.10.
  res <- bounds(
    events = c(28, 42, 70),
    power_int = 0.9,
    falsepos = 0.1,
    hr_null = 1.3,
    hr_alt = 0.7,
    hr_marg_benefit = 0.95
  )

  # check for errors in the provided results.
  expect_equal(as.numeric(res$summary$'OS HR threshold for positivity'),
               # OS HR threshold for positivity
               c(1.136, 1.040, 0.957),
               tolerance = TOLERANCE)
  expect_equal(as.numeric(res$summary$'One-sided false positive error rate'),
               # One-sided false positive error rate
               c(0.36, 0.23, 0.1),
               tolerance = TOLERANCE)
  expect_equal(as.numeric(res$summary$'Level of 2-sided CI needed to rule out delta null'),
               # Level of 2-sided CI needed to rule out
               c(28, 53, 80),
               tolerance = 1e-1)
  expect_equal(as.numeric(res$summary$'Probability of meeting positivity threshold under delta alt'),
               # Probability of meeting positivity threshold under δalt
               c(0.9, 0.9, 0.905),
               tolerance = TOLERANCE)
  expect_equal(as.numeric(res$summary$'Probability of meeting positivity threshold under incremental benefit'),
               # Probability of meeting positivity threshold under HR = 0.95
               c(0.682, 0.615 , 0.512),
               tolerance = TOLERANCE)
})


test_that("Table#4: Example 2", {
  # OS monitoring guideline with δnull = 1.3, δalt = 0.95, γFA = 0.2 and βPA = 0.25.
  res <- bounds(
    events = c(28, 42, 70),
    power_int = 0.75,
    falsepos = 0.2,
    hr_null = 1.3,
    hr_alt = 0.95,
    hr_marg_benefit = 1
  )

  # check for errors in the provided results.
  expect_equal(as.numeric(res$summary$'OS HR threshold for positivity'),
               # OS HR threshold for positivity
               c(1.226, 1.170, 1.063),
               tolerance = TOLERANCE)
  expect_equal(as.numeric(res$summary$'One-sided false positive error rate'),
               # One-sided false positive error rate
               c(0.438, 0.366, 0.2),
               tolerance = TOLERANCE)
  expect_equal(as.numeric(res$summary$'Level of 2-sided CI needed to rule out delta null'),
               # Level of 2-sided CI needed to rule out
               c(12, 27, 60),
               tolerance = 5e-1)
  expect_equal(as.numeric(res$summary$'Probability of meeting positivity threshold under delta alt'),
               # Probability of meeting positivity threshold under δalt
               c(0.750, 0.750, 0.681),
               tolerance = TOLERANCE)
  expect_equal(as.numeric(res$summary$'Probability of meeting positivity threshold under incremental benefit'),
               # Probability of meeting positivity threshold under HR = 1
               c(0.705 , 0.694, 0.601),
               tolerance = TOLERANCE)
})



test_that("Table#5: Example 2", {
  # OS monitoring guideline applied to Motivating Example 2 with δnull = 1.333, δalt = 0.7, γFA = 0.20 and βPA = 0.1.
  res <- bounds(
    events = c(22, 34),
    power_int = 0.9,
    falsepos = 0.20,
    hr_null = 4 / 3,
    hr_alt = 0.7,
    hr_marg_benefit = 0.95
  )

  # check for errors in the provided results.
  expect_equal(as.numeric(res$summary$'OS HR threshold for positivity'),
               # OS HR threshold for positivity
               c(1.209, 0.999),
               tolerance = TOLERANCE)
  expect_equal(as.numeric(res$summary$'One-sided false positive error rate'),
               # One-sided false positive error rate
               c(0.409, 0.2),
               tolerance = TOLERANCE)
  expect_equal(as.numeric(res$summary$'Level of 2-sided CI needed to rule out delta null'),
               # Level of 2-sided CI needed to rule out
               c(18, 60),
               tolerance = 1e-1)
  expect_equal(as.numeric(res$summary$'Probability of meeting positivity threshold under delta alt'),  # Probability of meeting positivity threshold under δalt
               c(0.9, 0.85),
               tolerance = TOLERANCE)
  expect_equal(as.numeric(res$summary$'Probability of meeting positivity threshold under incremental benefit'),
               # Probability of meeting positivity threshold under HR = 0.95
               c(0.714 , 0.558),
               tolerance = TOLERANCE)
})