tests/testthat/test-ADSSMod.r
In medianasoft/MedianaDesigner: Power and Sample Size Calculations for Clinical Trials
commonNSim = 50
isTestMultiCore = FALSE
# Normal case parameters
normalCase = list(
# Endpoint type
endpoint_type = "Normal",
# Direction of favorable outcome (Higher or Lower)
# Default: Higher
#direction = "Higher",
# Number of enrolled patients (control, treatment)
sample_size = c(120, 120),
# Annual patient dropout rate based on an exponential dropout distribution
dropout_rate = 0.05,
# Mean and SD in the control arm
control_mean = 0,
control_sd = 1,
# Mean and SD in the treatment arm
treatment_mean = 0.3,
treatment_sd = 1,
# Information fractions at IA1, IA2, FA (before sample size adjustment) and FA (after sample size adjustment)
info_frac = c(0.4, 0.6, 1, 1.3),
# Futility threshold for conditional power at IA1
futility_threshold = 0.1,
# Promising interval for conditional power at IA2
promising_interval = c(0.5, 0.9),
# Target conditional power for increasing the number of events at IA2
target_power = 0.9,
# One-sided alpha level
alpha = 0.025,
# Number of simulations
nsims = commonNSim
)
# Binary case parameters
binaryCase = list(
# Endpoint type
endpoint_type = "Binary",
# Direction of favorable outcome (Higher or Lower)
# Default: Higher
direction = "Higher",
# Number of enrolled patients (control, treatment)
sample_size = c(100, 200),
# Patient dropout rate
dropout_rate = 0.1,
# Response rate in the control arm
control_rate = 0.1,
# Response rate in the treatment arm
treatment_rate = 0.25,
# Information fractions at IA1, IA2, FA (before sample size adjustment)
# and FA (after sample size adjustment)
info_frac = c(0.4, 0.6, 1, 1.4),
# Futility threshold for conditional power at IA1
futility_threshold = 0.2,
# Promising interval for conditional power at IA2
promising_interval = c(0.5, 0.9),
# Target conditional power for increasing the sample size at IA2
target_power = 0.9,
# One-sided alpha level
alpha = 0.025,
# Number of simulations
nsims = commonNSim
)
# Time-to-event case parameters
timeToEventCase = list(
# Endpoint type
endpoint_type = "Time-to-event",
# Direction of favorable outcome (Higher or Lower)
# Default: Higher
direction = "Higher",
# Number of enrolled patients (control, treatment)
sample_size = c(220, 220),
# Annual patient dropout rate
dropout_rate = 0.05,
# Median time in the control arm
control_time = 7.5,
# Median time in the treatment arm
treatment_time = 10.5,
# Target event count at FA (before event count adjustment)
event_count = 300,
# Information fractions at IA1, IA2, FA (before event count adjustment)
# and FA (after event count adjustment)
info_frac = c(0.4, 0.6, 1, 1.3),
# Futility threshold for conditional power at IA1
futility_threshold = 0.1,
# Promising interval for conditional power at IA2
promising_interval = c(0.5, 0.9),
# Target conditional power for increasing the number of events at IA2
target_power = 0.9,
# Enrollment period
enrollment_period = 12,
# Median enrollment time
enrollment_parameter = 8,
# One-sided alpha level
alpha = 0.025,
# Number of simulations
nsims = commonNSim
)
context("ADSSMod - Success runs")
checkExpectationsForNormalCase = function(results) {
expect_s3_class(results, "ADSSModResults")
expect_type(results$sim_results, "double")
expect_length(results$sim_results, 19000)
sim_summary = results$sim_summary
# print(sim_summary)
expect_type(sim_summary, "list")
expect_equal(sim_summary$futility, 0.19, tolerance=0.1)
expect_equal(sim_summary$increase, 0.199, tolerance=0.1)
expect_equal(sim_summary$ad_power, 0.628, tolerance=0.1)
expect_equal(sim_summary$ad_under, 0.227, tolerance=0.1)
expect_equal(sim_summary$ad_prom, 0.849, tolerance=0.1)
expect_equal(sim_summary$ad_over, 0.96, tolerance=0.1)
expect_equal(sim_summary$trad_under, 0.227, tolerance=0.1)
expect_equal(sim_summary$trad_prom, 0.718, tolerance=0.1)
expect_equal(sim_summary$trad_over, 0.960, tolerance=0.1)
}
test_that("Success run ADSSMod with Normal case (single core)", {
# Success run with check default values
results = ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
# check defaults:
#dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
# check defaults:
#alpha = normalCase$alpha,
#nsims = normalCase$nsims
# Run once with random seek
random_seed = 49283
)
)
checkExpectationsForNormalCase(results)
# Check for report generation
ADSSModReportDoc(results)
GenerateReport(results, tempfile(fileext = ".docx"))
})
if (isTestMultiCore) {
test_that("Success run ADSSMod with Normal case (two cores)", {
# Success run with check default values
params = normalCase
params$ncores = 2
params$nsims = 1000
results = ADSSMod(params)
checkExpectationsForNormalCase(results)
})
}
test_that("Success run ADSSMod with Binary case", {
# Success run
results = ADSSMod(binaryCase)
expect_type(results$sim_results, "double")
expect_type(results$sim_summary, "list")
expect_length(results$sim_results, 19 * binaryCase$nsims)
expect_true(abs(results$sim_summary$futility - 0.2) < 0.2)
expect_true(abs(results$sim_summary$increase - 0.2) < 0.2)
expect_true(abs(results$sim_summary$ad_power - 0.8) < 0.2)
expect_true(abs(results$sim_summary$ad_prom - 0.9) < 0.2)
expect_true(abs(results$sim_summary$ad_over - 0.9) < 0.2)
expect_true(abs(results$sim_summary$trad_prom - 0.8) < 0.3)
expect_true(abs(results$sim_summary$trad_over - 0.9) < 0.2)
# Check for report generation
ADSSModReportDoc(results)
})
test_that("Success run ADSSMod with Time-to-event case", {
# Success run
results = ADSSMod(timeToEventCase)
expect_type(results$sim_results, "double")
expect_type(results$sim_summary, "list")
expect_length(results$sim_results, 19 * binaryCase$nsims)
expect_true(abs(results$sim_summary$futility - 0.2) < 0.2)
expect_true(abs(results$sim_summary$increase - 0.2) < 0.2)
expect_true(abs(results$sim_summary$ad_power - 0.8) < 0.2)
expect_true(abs(results$sim_summary$ad_prom - 0.9) < 0.2)
expect_true(abs(results$sim_summary$ad_over - 0.9) < 0.2)
expect_true(abs(results$sim_summary$trad_prom - 0.8) < 0.3)
expect_true(abs(results$sim_summary$trad_over - 0.9) < 0.2)
# Check for report generation
ADSSModReportDoc(results)
})
test_that("Success run ADSSMod with Time-to-event case with Lower direction", {
# Success run
results = ADSSMod(
list(
endpoint_type = timeToEventCase$endpoint_type,
direction = "Lower",
sample_size = timeToEventCase$sample_size,
dropout_rate = timeToEventCase$dropout_rate,
control_time = timeToEventCase$control_time,
treatment_time = timeToEventCase$treatment_time,
event_count = timeToEventCase$event_count,
info_frac = timeToEventCase$info_frac,
futility_threshold = timeToEventCase$futility_threshold,
promising_interval = timeToEventCase$promising_interval,
target_power = timeToEventCase$target_power,
enrollment_period = timeToEventCase$enrollment_period,
enrollment_parameter = timeToEventCase$enrollment_parameter,
alpha = timeToEventCase$alpha,
nsims = timeToEventCase$nsims
)
)
expect_type(results$sim_results, "double")
expect_type(results$sim_summary, "list")
expect_length(results$sim_results, 19 * binaryCase$nsims)
# Check for report generation
ADSSModReportDoc(results)
})
context("ADSSMod - Error checks")
test_that("Input parameters errors check ADSSMod", {
# Errors check
expect_error(
ADSSMod(
c("Not a list")
),
info = "Checking for wrong parameters collection type"
)
expect_error(
ADSSMod(
list(
# miss
# endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for missing endpoint type"
)
expect_error(
ADSSMod(
list(
# wrong
endpoint_type = "SomeStrangeType",
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for incorrect value of endpoint type"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
# wrong
direction = "Wrong", # normalCase$direction,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for incorrect value of direction"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# missing
#info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for missing information fractions"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# invalid value
info_frac = c(0.4, 0.6, 1), # Right value is c(0.4, 0.6, 1, 1.3)
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for invalid information fractions"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# invalid value
info_frac = c(0.6, 0.4, 1, 1.3), # Right value is c(0.4, 0.6, 1, 1.3)
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for inconsistent information fractions (first value greater than second)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# invalid value
info_frac = c(0.4, 1.1, 1, 1.3), # Right value is c(0.4, 0.6, 1, 1.3)
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for inconsistent information fractions (second value greater than third)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# invalid value
info_frac = c(0.4, 0.6, 0.9, 1.3), # Right value is c(0.4, 0.6, 1, 1.3)
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for inconsistent information fractions (third value is not 1)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# invalid value
info_frac = c(0.4, 0.6, 1, 0.8), # Right value is c(0.4, 0.6, 1, 1.3)
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for inconsistent information fractions (third value greater than forth)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.4, 0.6, 1, 1.3, 1.5), # c(0.4, 0.6, 1, 1.3) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong Information fractions at IA1, IA2, FA (incorrect value size)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0, 0.6, 1, 1.3), # c(0.4, 0.6, 1, 1.3) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong Information fractions at IA1, IA2, FA (value <= 0)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.4, 0.6, 1, 3.001), # c(0.4, 0.6, 1, 1.3) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong Information fractions at IA1, IA2, FA (value > 3)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
# wrong
promising_interval = c(0.5), #normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong promising interval (short vector)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
# wrong
promising_interval = c(0.9, 0.5), #normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong promising interval (first value greater than second)"
)
testParameterErrors = function(params, paramName, paramDesc,
checkMissing = TRUE, checkSize = TRUE, checkMin = NA, checkMax = NA) {
func = ADSSMod
paramDesc = paste0(paramDesc, " (", paramName, ")")
if (!is.null(params$endpoint_type))
paramDesc = paste0(params$endpoint_type, ": ", paramDesc)
# Missing
if (checkMissing) {
testParams = params
testParams[paramName] <- NULL
expect_error(func(testParams),
info = paste0("Checking for missing ", paramDesc))
}
# Check size
if (checkSize) {
testParams = params
testParams[[paramName]] <- append(testParams[[paramName]], testParams[[paramName]][1])
expect_error(func(testParams),
info = paste0("Checking for wrong ", paramDesc, " (incorrect value size)"))
}
# Check below min value
if (!is.null(checkMin) && !is.na(checkMin)) {
testParams = params
testParams[[paramName]][1] <- checkMin
expect_error(func(testParams),
info = paste0("Checking for wrong ", paramDesc, " (incorrect value < min)"))
}
# Check under max value
if (!is.null(checkMax) && !is.na(checkMax)) {
testParams = params
testParams[[paramName]][length(testParams[[paramName]])] <- checkMax
expect_error(func(testParams),
info = paste0("Checking for wrong ", paramDesc, " (incorrect value > max)"))
}
}
testParameterErrors(normalCase,
'sample_size',
'Number of enrolled patients',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 1001)
testParameterErrors(normalCase,
'futility_threshold',
'Futility threshold at IA1',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = -0.001,
checkMax = 0.999)
testParameterErrors(normalCase,
'promising_interval',
'Promising interval at IA2',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0.001,
checkMax = 0.999)
testParameterErrors(normalCase,
'target_power',
'Target conditional power',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0.001,
checkMax = 0.999)
testParameterErrors(normalCase,
'dropout_rate',
'Patient dropout rate',
checkMissing = FALSE,
checkSize = TRUE,
checkMin = -0.001,
checkMax = 1)
testParameterErrors(normalCase,
'nsims',
'Number of simulations',
checkMissing = FALSE,
checkSize = TRUE,
checkMin = 0,
checkMax = 10001)
testParameterErrors(normalCase,
'alpha',
'One-sided Type I error rate',
checkMissing = FALSE,
checkSize = TRUE,
checkMin = 0.001,
checkMax = 0.5)
testParameterErrors(normalCase,
'control_mean',
'Mean effect in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = NA,
checkMax = NA)
testParameterErrors(normalCase,
'treatment_mean',
'Mean effect in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = NA,
checkMax = NA)
testParameterErrors(normalCase,
'control_sd',
'Standard deviation in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(normalCase,
'treatment_sd',
'Standard deviation in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(binaryCase,
'control_rate',
'Response rate in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 1)
testParameterErrors(binaryCase,
'treatment_rate',
'Response rate in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 1)
testParameterErrors(timeToEventCase,
'control_time',
'Median time in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(timeToEventCase,
'treatment_time',
'Median time in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(timeToEventCase,
'enrollment_period',
'Patient enrollment period',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(timeToEventCase,
'enrollment_parameter',
'Median enrollment time',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = timeToEventCase$enrollment_period)
testParameterErrors(timeToEventCase,
'event_count',
'Target number of events at FA',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = sum(timeToEventCase$sample_size))
})
test_that("Input parameters errors check ADSSModReportDoc", {
expect_error(
ADSSModReportDoc(""),
info = "Checking for wrong parameter type for report generator"
)
})
medianasoft/MedianaDesigner documentation built on Aug. 28, 2023, 6:33 p.m.
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commonNSim = 50
isTestMultiCore = FALSE
# Normal case parameters
normalCase = list(
# Endpoint type
endpoint_type = "Normal",
# Direction of favorable outcome (Higher or Lower)
# Default: Higher
#direction = "Higher",
# Number of enrolled patients (control, treatment)
sample_size = c(120, 120),
# Annual patient dropout rate based on an exponential dropout distribution
dropout_rate = 0.05,
# Mean and SD in the control arm
control_mean = 0,
control_sd = 1,
# Mean and SD in the treatment arm
treatment_mean = 0.3,
treatment_sd = 1,
# Information fractions at IA1, IA2, FA (before sample size adjustment) and FA (after sample size adjustment)
info_frac = c(0.4, 0.6, 1, 1.3),
# Futility threshold for conditional power at IA1
futility_threshold = 0.1,
# Promising interval for conditional power at IA2
promising_interval = c(0.5, 0.9),
# Target conditional power for increasing the number of events at IA2
target_power = 0.9,
# One-sided alpha level
alpha = 0.025,
# Number of simulations
nsims = commonNSim
)
# Binary case parameters
binaryCase = list(
# Endpoint type
endpoint_type = "Binary",
# Direction of favorable outcome (Higher or Lower)
# Default: Higher
direction = "Higher",
# Number of enrolled patients (control, treatment)
sample_size = c(100, 200),
# Patient dropout rate
dropout_rate = 0.1,
# Response rate in the control arm
control_rate = 0.1,
# Response rate in the treatment arm
treatment_rate = 0.25,
# Information fractions at IA1, IA2, FA (before sample size adjustment)
# and FA (after sample size adjustment)
info_frac = c(0.4, 0.6, 1, 1.4),
# Futility threshold for conditional power at IA1
futility_threshold = 0.2,
# Promising interval for conditional power at IA2
promising_interval = c(0.5, 0.9),
# Target conditional power for increasing the sample size at IA2
target_power = 0.9,
# One-sided alpha level
alpha = 0.025,
# Number of simulations
nsims = commonNSim
)
# Time-to-event case parameters
timeToEventCase = list(
# Endpoint type
endpoint_type = "Time-to-event",
# Direction of favorable outcome (Higher or Lower)
# Default: Higher
direction = "Higher",
# Number of enrolled patients (control, treatment)
sample_size = c(220, 220),
# Annual patient dropout rate
dropout_rate = 0.05,
# Median time in the control arm
control_time = 7.5,
# Median time in the treatment arm
treatment_time = 10.5,
# Target event count at FA (before event count adjustment)
event_count = 300,
# Information fractions at IA1, IA2, FA (before event count adjustment)
# and FA (after event count adjustment)
info_frac = c(0.4, 0.6, 1, 1.3),
# Futility threshold for conditional power at IA1
futility_threshold = 0.1,
# Promising interval for conditional power at IA2
promising_interval = c(0.5, 0.9),
# Target conditional power for increasing the number of events at IA2
target_power = 0.9,
# Enrollment period
enrollment_period = 12,
# Median enrollment time
enrollment_parameter = 8,
# One-sided alpha level
alpha = 0.025,
# Number of simulations
nsims = commonNSim
)
context("ADSSMod - Success runs")
checkExpectationsForNormalCase = function(results) {
expect_s3_class(results, "ADSSModResults")
expect_type(results$sim_results, "double")
expect_length(results$sim_results, 19000)
sim_summary = results$sim_summary
# print(sim_summary)
expect_type(sim_summary, "list")
expect_equal(sim_summary$futility, 0.19, tolerance=0.1)
expect_equal(sim_summary$increase, 0.199, tolerance=0.1)
expect_equal(sim_summary$ad_power, 0.628, tolerance=0.1)
expect_equal(sim_summary$ad_under, 0.227, tolerance=0.1)
expect_equal(sim_summary$ad_prom, 0.849, tolerance=0.1)
expect_equal(sim_summary$ad_over, 0.96, tolerance=0.1)
expect_equal(sim_summary$trad_under, 0.227, tolerance=0.1)
expect_equal(sim_summary$trad_prom, 0.718, tolerance=0.1)
expect_equal(sim_summary$trad_over, 0.960, tolerance=0.1)
}
test_that("Success run ADSSMod with Normal case (single core)", {
# Success run with check default values
results = ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
# check defaults:
#dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
# check defaults:
#alpha = normalCase$alpha,
#nsims = normalCase$nsims
# Run once with random seek
random_seed = 49283
)
)
checkExpectationsForNormalCase(results)
# Check for report generation
ADSSModReportDoc(results)
GenerateReport(results, tempfile(fileext = ".docx"))
})
if (isTestMultiCore) {
test_that("Success run ADSSMod with Normal case (two cores)", {
# Success run with check default values
params = normalCase
params$ncores = 2
params$nsims = 1000
results = ADSSMod(params)
checkExpectationsForNormalCase(results)
})
}
test_that("Success run ADSSMod with Binary case", {
# Success run
results = ADSSMod(binaryCase)
expect_type(results$sim_results, "double")
expect_type(results$sim_summary, "list")
expect_length(results$sim_results, 19 * binaryCase$nsims)
expect_true(abs(results$sim_summary$futility - 0.2) < 0.2)
expect_true(abs(results$sim_summary$increase - 0.2) < 0.2)
expect_true(abs(results$sim_summary$ad_power - 0.8) < 0.2)
expect_true(abs(results$sim_summary$ad_prom - 0.9) < 0.2)
expect_true(abs(results$sim_summary$ad_over - 0.9) < 0.2)
expect_true(abs(results$sim_summary$trad_prom - 0.8) < 0.3)
expect_true(abs(results$sim_summary$trad_over - 0.9) < 0.2)
# Check for report generation
ADSSModReportDoc(results)
})
test_that("Success run ADSSMod with Time-to-event case", {
# Success run
results = ADSSMod(timeToEventCase)
expect_type(results$sim_results, "double")
expect_type(results$sim_summary, "list")
expect_length(results$sim_results, 19 * binaryCase$nsims)
expect_true(abs(results$sim_summary$futility - 0.2) < 0.2)
expect_true(abs(results$sim_summary$increase - 0.2) < 0.2)
expect_true(abs(results$sim_summary$ad_power - 0.8) < 0.2)
expect_true(abs(results$sim_summary$ad_prom - 0.9) < 0.2)
expect_true(abs(results$sim_summary$ad_over - 0.9) < 0.2)
expect_true(abs(results$sim_summary$trad_prom - 0.8) < 0.3)
expect_true(abs(results$sim_summary$trad_over - 0.9) < 0.2)
# Check for report generation
ADSSModReportDoc(results)
})
test_that("Success run ADSSMod with Time-to-event case with Lower direction", {
# Success run
results = ADSSMod(
list(
endpoint_type = timeToEventCase$endpoint_type,
direction = "Lower",
sample_size = timeToEventCase$sample_size,
dropout_rate = timeToEventCase$dropout_rate,
control_time = timeToEventCase$control_time,
treatment_time = timeToEventCase$treatment_time,
event_count = timeToEventCase$event_count,
info_frac = timeToEventCase$info_frac,
futility_threshold = timeToEventCase$futility_threshold,
promising_interval = timeToEventCase$promising_interval,
target_power = timeToEventCase$target_power,
enrollment_period = timeToEventCase$enrollment_period,
enrollment_parameter = timeToEventCase$enrollment_parameter,
alpha = timeToEventCase$alpha,
nsims = timeToEventCase$nsims
)
)
expect_type(results$sim_results, "double")
expect_type(results$sim_summary, "list")
expect_length(results$sim_results, 19 * binaryCase$nsims)
# Check for report generation
ADSSModReportDoc(results)
})
context("ADSSMod - Error checks")
test_that("Input parameters errors check ADSSMod", {
# Errors check
expect_error(
ADSSMod(
c("Not a list")
),
info = "Checking for wrong parameters collection type"
)
expect_error(
ADSSMod(
list(
# miss
# endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for missing endpoint type"
)
expect_error(
ADSSMod(
list(
# wrong
endpoint_type = "SomeStrangeType",
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for incorrect value of endpoint type"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
# wrong
direction = "Wrong", # normalCase$direction,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for incorrect value of direction"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# missing
#info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for missing information fractions"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# invalid value
info_frac = c(0.4, 0.6, 1), # Right value is c(0.4, 0.6, 1, 1.3)
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for invalid information fractions"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# invalid value
info_frac = c(0.6, 0.4, 1, 1.3), # Right value is c(0.4, 0.6, 1, 1.3)
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for inconsistent information fractions (first value greater than second)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# invalid value
info_frac = c(0.4, 1.1, 1, 1.3), # Right value is c(0.4, 0.6, 1, 1.3)
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for inconsistent information fractions (second value greater than third)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# invalid value
info_frac = c(0.4, 0.6, 0.9, 1.3), # Right value is c(0.4, 0.6, 1, 1.3)
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for inconsistent information fractions (third value is not 1)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# invalid value
info_frac = c(0.4, 0.6, 1, 0.8), # Right value is c(0.4, 0.6, 1, 1.3)
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for inconsistent information fractions (third value greater than forth)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.4, 0.6, 1, 1.3, 1.5), # c(0.4, 0.6, 1, 1.3) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong Information fractions at IA1, IA2, FA (incorrect value size)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0, 0.6, 1, 1.3), # c(0.4, 0.6, 1, 1.3) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong Information fractions at IA1, IA2, FA (value <= 0)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.4, 0.6, 1, 3.001), # c(0.4, 0.6, 1, 1.3) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
promising_interval = normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong Information fractions at IA1, IA2, FA (value > 3)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
# wrong
promising_interval = c(0.5), #normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong promising interval (short vector)"
)
expect_error(
ADSSMod(
list(
endpoint_type = normalCase$endpoint_type,
sample_size = normalCase$sample_size,
dropout_rate = normalCase$dropout_rate,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
info_frac = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
# wrong
promising_interval = c(0.9, 0.5), #normalCase$promising_interval,
target_power = normalCase$target_power,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong promising interval (first value greater than second)"
)
testParameterErrors = function(params, paramName, paramDesc,
checkMissing = TRUE, checkSize = TRUE, checkMin = NA, checkMax = NA) {
func = ADSSMod
paramDesc = paste0(paramDesc, " (", paramName, ")")
if (!is.null(params$endpoint_type))
paramDesc = paste0(params$endpoint_type, ": ", paramDesc)
# Missing
if (checkMissing) {
testParams = params
testParams[paramName] <- NULL
expect_error(func(testParams),
info = paste0("Checking for missing ", paramDesc))
}
# Check size
if (checkSize) {
testParams = params
testParams[[paramName]] <- append(testParams[[paramName]], testParams[[paramName]][1])
expect_error(func(testParams),
info = paste0("Checking for wrong ", paramDesc, " (incorrect value size)"))
}
# Check below min value
if (!is.null(checkMin) && !is.na(checkMin)) {
testParams = params
testParams[[paramName]][1] <- checkMin
expect_error(func(testParams),
info = paste0("Checking for wrong ", paramDesc, " (incorrect value < min)"))
}
# Check under max value
if (!is.null(checkMax) && !is.na(checkMax)) {
testParams = params
testParams[[paramName]][length(testParams[[paramName]])] <- checkMax
expect_error(func(testParams),
info = paste0("Checking for wrong ", paramDesc, " (incorrect value > max)"))
}
}
testParameterErrors(normalCase,
'sample_size',
'Number of enrolled patients',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 1001)
testParameterErrors(normalCase,
'futility_threshold',
'Futility threshold at IA1',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = -0.001,
checkMax = 0.999)
testParameterErrors(normalCase,
'promising_interval',
'Promising interval at IA2',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0.001,
checkMax = 0.999)
testParameterErrors(normalCase,
'target_power',
'Target conditional power',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0.001,
checkMax = 0.999)
testParameterErrors(normalCase,
'dropout_rate',
'Patient dropout rate',
checkMissing = FALSE,
checkSize = TRUE,
checkMin = -0.001,
checkMax = 1)
testParameterErrors(normalCase,
'nsims',
'Number of simulations',
checkMissing = FALSE,
checkSize = TRUE,
checkMin = 0,
checkMax = 10001)
testParameterErrors(normalCase,
'alpha',
'One-sided Type I error rate',
checkMissing = FALSE,
checkSize = TRUE,
checkMin = 0.001,
checkMax = 0.5)
testParameterErrors(normalCase,
'control_mean',
'Mean effect in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = NA,
checkMax = NA)
testParameterErrors(normalCase,
'treatment_mean',
'Mean effect in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = NA,
checkMax = NA)
testParameterErrors(normalCase,
'control_sd',
'Standard deviation in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(normalCase,
'treatment_sd',
'Standard deviation in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(binaryCase,
'control_rate',
'Response rate in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 1)
testParameterErrors(binaryCase,
'treatment_rate',
'Response rate in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 1)
testParameterErrors(timeToEventCase,
'control_time',
'Median time in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(timeToEventCase,
'treatment_time',
'Median time in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(timeToEventCase,
'enrollment_period',
'Patient enrollment period',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(timeToEventCase,
'enrollment_parameter',
'Median enrollment time',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = timeToEventCase$enrollment_period)
testParameterErrors(timeToEventCase,
'event_count',
'Target number of events at FA',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = sum(timeToEventCase$sample_size))
})
test_that("Input parameters errors check ADSSModReportDoc", {
expect_error(
ADSSModReportDoc(""),
info = "Checking for wrong parameter type for report generator"
)
})
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