test_that("summariseCohortOverlap", {
  skip_on_cran()
  person <- dplyr::tibble(
    person_id = c(1:20, 199),
    gender_concept_id = 8532,
    year_of_birth = runif(n=21, min=1950, max=2000),
    month_of_birth = runif(n=21, min=1, max=12),
    day_of_birth = runif(n=21, min=1, max=28),
    race_concept_id= 0,
    ethnicity_concept_id = 0
  )

  table <- dplyr::tibble(
    cohort_definition_id = c(rep(1, 15), rep(2, 10), rep(3, 15), rep(4, 5), 5),
    subject_id = c(20, 5, 10, 12, 4, 15, 2, 1, 5, 10, 5, 8, 13, 4, 10,
                   6, 18, 5, 1, 20, 14, 13, 8, 17, 3,
                   16, 15, 20, 17, 3, 14, 6, 11, 8, 7, 20, 19, 5, 2, 18,
                   5, 12, 3, 14, 13, 199),
    cohort_start_date = as.Date(c(rep("2000-01-01",5), rep("2010-09-05",5), rep("2006-05-01",5),
                                  rep("2003-03-31",5), rep("2008-07-02",5), rep("2000-01-01",5),
                                  rep("2012-09-05",5), rep("1996-05-01",5), rep("1989-03-31",5), "1989-03-31")),
    cohort_end_date = as.Date(c(rep("2000-01-01",5), rep("2010-09-05",5), rep("2006-05-01",5),
                                rep("2003-03-31",5), rep("2008-07-02",5), rep("2000-01-01",5),
                                rep("2012-09-05",5), rep("1996-05-01",5), rep("1989-03-31",5), "1989-03-31"))
  )

  obs <- dplyr::tibble(
    observation_period_id = c(1:20, 199),
    person_id = c(1:20, 199),
    observation_period_start_date = as.Date("1930-01-01"),
    observation_period_end_date =  as.Date("2025-01-01"),
    period_type_concept_id = NA
  )

  cdm <- mockPatientProfiles(person = person, observation_period = obs, table = table)

  overlap1 <- summariseCohortOverlap(cdm$table)
  expect_equal(omopgenerics::resultColumns("summarised_result"),
               colnames(overlap1))
  expect_equal(overlap1$group_name |> unique(),
               "cohort_name_reference &&& cohort_name_comparator")
  expect_true(nrow(overlap1) == 5*4*6)
  expect_true(overlap1 |> dplyr::filter(grepl("cohort_5", .data$group_level)) |> dplyr::pull(estimate_value) |> unique() == "0")
  expect_equal(unique(overlap1$strata_name), "overall")
  expect_equal(unique(overlap1$strata_level), "overall")
  expect_equal(unique(overlap1$additional_name), "overall")
  expect_equal(unique(overlap1$additional_level), "overall")
  expect_true(all(c("count", "percentage") %in% unique(overlap1$estimate_name)))


  # strata and cohortID ----
  overlap2 <- summariseCohortOverlap(cdm$table,
                                     cohortId = 1:2)
  expect_true(nrow(overlap2) == 2*6)
  expect_true(all(
    c("cohort_2 &&& cohort_1", "cohort_1 &&& cohort_2") %in%
    unique(overlap2$group_level)))

  cdm$table <- cdm$table |>
    addAge(ageGroup = list(c(0,40), c(41,100))) |>
    addSex() |>
    dplyr::compute(name = "table", temporary = FALSE) |>
    omopgenerics::newCohortTable()

  s <- cdm$table |> dplyr::filter(cohort_definition_id %in% 1:2) |> dplyr::distinct(age_group, sex)
  s1 <- nrow(s |> dplyr::collect())
  s2 <- nrow(s |> dplyr::distinct(age_group) |> dplyr::collect())

  overlap3 <- summariseCohortOverlap(cdm$table,
                                     cohortId = 1:2,
                                     strata = list("age_group", c("age_group", "sex")))
  expect_true(all(unique(overlap3$group_level) %in%
                    unique(overlap1$group_level)))
  expect_true(all(c("overall", "age_group", "age_group &&& sex") %in%
                    unique(overlap3$strata_name)))
  expect_true(nrow(overlap3) == 2*6 + 2*6*s1 + 2*6*s2)

  CDMConnector::cdm_disconnect(cdm)
})