toInteger.R ind test

tests/testthat/test-independent-test-toInteger.R

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+test_that("toInteger() handles valid gsDesign inputs correctly", {
+  # a gsSurv object
+  x <- gsSurv(
+    k = 3,                 # 3 analyses
+    test.type = 4,         # Non-binding futility bound 1 (no futility bound) and 4 are allowable
+    alpha = .025,          # 1-sided Type I error
+    beta = .1,             # Type II error (1 - power)
+    timing = c(0.45, 0.7), # Proportion of final planned events at interims
+    sfu = sfHSD,           # Efficacy spending function
+    sfupar = -4,           # Parameter for efficacy spending function
+    sfl = sfLDOF,          # Futility spending function; not needed for test.type = 1
+    sflpar = 0,            # Parameter for futility spending function
+    lambdaC = .001,        # Exponential failure rate
+    hr = 0.3,              # Assumed proportional hazard ratio (1 - vaccine efficacy = 1 - VE)
+    hr0 = 0.7,             # Null hypothesis VE
+    eta = 5e-04,           # Exponential dropout rate
+    gamma = 10,            # Piecewise exponential enrollment rates
+    R = 16,                # Time period durations for enrollment rates in gamma
+    T = 24,                # Planned trial duration
+    minfup = 8,            # Planned minimum follow-up
+    ratio = 3              # Randomization ratio (experimental:control)
+  )
+
+  # Test toInteger function with the generated gsSurv object
+  result <- toInteger(x, ratio = 3)
+
+  # Check that the output is of class gsSurv and gsDesign
+  expect_s3_class(result, c("gsSurv", "gsDesign"))
+
+  # Test that the counts are rounded integers
+  expect_true(all(result$n.I == round(result$n.I)))
+
+  # Ensure final count is rounded up correctly when roundUpFinal is TRUE
+  expect_equal(result$n.I[x$k], ceiling(x$n.I[x$k]))
+
+})
+
+
+test_that("toInteger() handles different ratio values correctly", {
+  # Create a gsSurv object
+  x <- gsSurv(
+    k = 3,
+    test.type = 4,
+    alpha = .025,
+    beta = .1,
+    timing = c(0.45, 0.7),
+    sfu = sfHSD,
+    sfupar = -4,
+    sfl = sfLDOF,
+    sflpar = 0,
+    lambdaC = .001,
+    hr = 0.3,
+    hr0 = 0.7,
+    eta = 5e-04,
+    gamma = 10,
+    R = 16,
+    T = 24,
+    minfup = 8,
+    ratio = 2
+  )
+
+  # Test with a different ratio
+  result <- toInteger(x, ratio = 2)
+
+  # Test if the final sample size is a multiple of ratio + 1
+  expect_true(result$n.I[x$k] %% (2 + 1) == 0)
+
+  # Ensure final count is rounded up correctly when roundUpFinal is TRUE
+  expect_equal(result$n.I[x$k], ceiling(x$n.I[x$k]))
+})
+
+test_that("toInteger() handles non-survival gsDesign objects", {
+  # Create a non-survival gsDesign object
+  x <- gsDesign(
+    k = 3,
+    test.type = 2,
+    alpha = 0.025,
+    beta = 0.1,
+    delta = 0.5
+  )
+
+  # Test the toInteger function with the non-survival design
+  result <- toInteger(x, ratio = 1)
+
+  # Check if the output retains the class gsDesign
+  expect_s3_class(result, "gsDesign")
+
+  # Ensure the final count is rounded up to the nearest multiple of (ratio + 1)
+  expect_true(result$n.I[x$k] %% (1 + 1) == 0)
+
+})
+
+
+test_that("toInteger() raises an error when n.I contains negative values", {
+  # Create a gsDesign object with arbitrary settings
+  x_test <- gsDesign(
+    k = 3,                 # 3 analyses
+    test.type = 2,         # Binding futility bound
+    alpha = .025,          # 1-sided Type I error
+    beta = .1,             # Type II error
+    sfu = sfHSD,           # Efficacy spending function
+    sfupar = -4            # Parameter for efficacy spending function
+  )
+
+  # Set n.I with a negative value
+  x_test$n.I <- c(100, 200, -250.5)  # Negative value to trigger the error
+
+  # Check that toInteger raises an error
+  expect_error(
+    toInteger(x_test, ratio = 3, roundUpFinal = TRUE),
+    regexp = "maxn.IPlan not on interval \\[0, Inf\\]",
+    info = "toInteger should raise an error when n.I contains negative values"
+  )
+})
+
+
+test_that("toInteger() raises an error for invalid ratio values", {
+
+  # Create a gsDesign object with arbitrary settings
+  x_test <- gsDesign(
+    k = 3,                 # 3 analyses
+    test.type = 1,         # Non-binding futility bound
+    alpha = .025,          # 1-sided Type I error
+    beta = .1,             # Type II error
+    sfu = sfHSD,           # Efficacy spending function
+    sfupar = -4            # Parameter for efficacy spending function
+  )
+
+  # Test for negative ratio
+  expect_error(
+    toInteger(x_test, ratio = -1, roundUpFinal = TRUE),
+    regexp = "n.I must be an increasing, positive sequence",
+    info = "toInteger should raise an error for negative ratio"
+  )
+
+  # Test for non-integer ratio (numeric)
+  expect_error(
+    toInteger(x_test, ratio = 2.5, roundUpFinal = TRUE),
+    regexp = "n.I must be an increasing, positive sequence",
+    info = "toInteger should raise an error for non-integer ratio"
+  )
+
+  # Test for non-integer ratio (character)
+  expect_error(
+    toInteger(x_test, ratio = "two", roundUpFinal = TRUE),
+    regexp = "n.I must be an increasing, positive sequence",
+    info = "toInteger should raise an error for character ratio"
+  )
+
+  # Test for NULL ratio
+  expect_error(
+    toInteger(x_test, ratio = NULL, roundUpFinal = TRUE),
+    regexp = "n.I must be an increasing, positive sequence",
+    info = "toInteger should raise an error for NULL ratio"
+  )
+})