tests/testthat/test-ADPopSel.r
In medianasoft/MedianaDesigner: Power and Sample Size Calculations for Clinical Trials
commonNSim = 50
isTestMultiCore = FALSE
# Normal case parameters
normalCase = list(
# Number of enrolled patients in each trial arm
sample_size = c(120, 120),
# Prevalence
prevalence = 0.4,
# Primary endpoint's type
endpoint_type = "Normal",
# Direction of favorable outcome (Higher or Lower)
# Default: Higher
#direction = "Higher",
# Mean and SD in the control arm
control_mean = c(0, 0),
control_sd = c(1, 1),
# Mean and SD in the treatment arm
treatment_mean = c(0.2, 0.4),
treatment_sd = c(1, 1),
# Information fractions at IA1, IA2, FA
info_frac = c(0.4, 0.6, 1.0),
# Futility threshold
futility_threshold = 0.1,
# Influence threshold for population selection
influence = 0.1,
# Interaction threshold for population selection
interaction = 1.3,
# Dropout rate at the end of the treatment period
dropout_rate = 0.05,
# One-sided Type I error rate
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(120, 120),
# Prevalence of biomarker-positive patients in the overall population
prevalence = 0.4,
# Patient dropout rate
dropout_rate = 0.15,
# Response rate in the control arm (biomarker-negative, biomarker-positive)
control_rate = c(0.1, 0.1),
# Response rate in the treatment arm (biomarker-negative, biomarker-positive)
treatment_rate = c(0.25, 0.4),
# Information fractions at IA1, IA2, FA
info_frac = c(0.4, 0.6, 1),
# Futility threshold for conditional power at IA1
futility_threshold = 0.2,
# Influence threshold at IA2
influence = 0.1,
# Interaction threshold at IA2
interaction = 1.3,
# 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(230, 230),
# Prevalence of biomarker-positive patients in the overall population
prevalence = 0.4,
# Annual patient dropout rate
dropout_rate = 0.05,
# Median times in the control arm (biomarker-negative, biomarker-positive)
control_time = c(7.5, 7.5),
# Median times in the treatment arm (biomarker-negative, biomarker-positive)
treatment_time = c(9.5, 11),
# Information fractions at IA1, IA2, FA
info_frac = c(0.4, 0.6, 1),
# Target event count at FA (overall, biomarker-positive)
event_count = c(300, 150),
# Futility threshold for conditional power at IA1
futility_threshold = 0.1,
# Influence threshold at IA2
influence = 0.1,
# Interaction threshold at IA2
interaction = 1.3,
# Enrollment period
enrollment_period = 12,
# Median enrollment time
enrollment_parameter = 8,
# One-sided alpha level
alpha = 0.025,
# Number of simulations
nsims = commonNSim
)
context("ADPopSel - Success runs")
checkExpectationsForNormalCase = function(results) {
expect_s3_class(results, "ADPopSelResults")
expect_type( results$sim_results, "double")
expect_length(results$sim_results, 15 * results$parameters$nsims)
sim_summary = results$sim_summary
# print(sim_summary)
expect_type(sim_summary, "list")
expect_equal(sim_summary$futility, 0.209, tolerance=0.1)
expect_is( sim_summary$trad_power, "numeric")
expect_length(sim_summary$trad_power, 2)
expect_equal( unname(sim_summary$trad_power[1]), 0.541, tolerance=0.1)
expect_equal( unname(sim_summary$trad_power[2]), 0.463, tolerance=0.1)
expect_is( sim_summary$ad_power, "numeric")
expect_length(sim_summary$ad_power, 3)
expect_equal( unname(sim_summary$ad_power[1]), 0.458, tolerance=0.1)
expect_equal( unname(sim_summary$ad_power[2]), 0.330, tolerance=0.1)
expect_equal( unname(sim_summary$ad_power[3]), 0.483, tolerance=0.1)
expect_is( sim_summary$hypothesis_selection, "numeric")
expect_length(sim_summary$hypothesis_selection, 3)
expect_equal( unname(sim_summary$hypothesis_selection[1]), 0.332, tolerance=0.1)
expect_equal( unname(sim_summary$hypothesis_selection[2]), 0.323, tolerance=0.1)
expect_equal( unname(sim_summary$hypothesis_selection[3]), 0.345, tolerance=0.1)
expect_is( sim_summary$look_time, "numeric")
expect_length(sim_summary$look_time, 4)
expect_equal( unname(sim_summary$look_time[1]), 0, tolerance=0.1)
expect_equal( unname(sim_summary$look_time[2]), 0, tolerance=0.1)
expect_true(is.nan(results$sim_summary$look_time[3]))
expect_true(is.nan(results$sim_summary$look_time[4]))
}
test_that("Success run ADPopSel with Normal case (single core)", {
# Success run
results = ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$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$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
# missing, use default 0
#dropout_rate = normalCase$dropout_rate,
# missing, use default 0.025
#alpha = normalCase$alpha,
# missing, use default 1000
#nsims = 1000 # normalCase$nsims
# Run once with random seed
random_seed = 49283
)
)
checkExpectationsForNormalCase(results)
# Check for report generation
ADPopSelReportDoc(results)
GenerateReport(results, tempfile(fileext = ".docx"))
})
if (isTestMultiCore) {
test_that("Success run ADPopSel with Normal case (two cores)", {
# Success run
params_for_run = normalCase
params_for_run$ncores = 2
results = ADPopSel(params_for_run)
checkExpectationsForNormalCase(results)
})
}
test_that("Success run ADPopSel with direction Lower", {
# Check for non-fail Lower direction success
# Success run
results = ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
direction = "Lower",
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,
influence = normalCase$influence,
interaction = normalCase$interaction#,
# missing, use default 0
#dropout_rate = normalCase$dropout_rate,
# missing, use default 0.025
#alpha = normalCase$alpha,
# missing, use default 1000
#nsims = 1000 # normalCase$nsims
)
)
expect_is(results, "ADPopSelResults")
ADPopSelReportDoc(results)
})
test_that("Success run ADPopSel with direction Lower & endPoint=3", {
# Check for non-fail Lower direction success
# Success run
results = ADPopSel(
list( # timeToEventCase
endpoint_type = timeToEventCase$endpoint_type,
direction = "Lower",
sample_size = timeToEventCase$sample_size,
prevalence = timeToEventCase$prevalence,
dropout_rate = timeToEventCase$dropout_rate,
control_time = timeToEventCase$control_time,
treatment_time = timeToEventCase$treatment_time,
info_frac = timeToEventCase$info_frac,
event_count = timeToEventCase$event_count,
futility_threshold = timeToEventCase$futility_threshold,
influence = timeToEventCase$influence,
interaction = timeToEventCase$interaction,
enrollment_period = timeToEventCase$enrollment_period,
enrollment_parameter = timeToEventCase$enrollment_parameter,
alpha = timeToEventCase$alpha,
nsims = timeToEventCase$nsims
)
)
expect_is(results, "ADPopSelResults")
ADPopSelReportDoc(results)
})
test_that("Success run ADPopSel with Binary case", {
# Success run
results = ADPopSel(binaryCase)
expect_is(results, "ADPopSelResults")
expect_type( results$sim_results, "double")
expect_length(results$sim_results, 15 * binaryCase$nsims)
expect_type( results$sim_summary, "list")
expect_true(abs(results$sim_summary$futility - 0.05) < 0.1)
expect_is( results$sim_summary$trad_power, "numeric")
expect_length( results$sim_summary$trad_power, 2)
expect_true(
abs(results$sim_summary$trad_power[1] - 0.92) < 0.1,
info = paste0("trad_power[1] is out of range (",results$sim_summary$trad_power[1],")"))
expect_true(
abs(results$sim_summary$trad_power[2] - 0.86) < 0.1,
info = paste0("trad_power[2] is out of range (",results$sim_summary$trad_power[2],")"))
expect_is( results$sim_summary$ad_power, "numeric")
expect_length( results$sim_summary$ad_power, 3)
expect_true(
abs(results$sim_summary$ad_power[1] - 0.82) < 0.1,
info = paste0("ad_power[1] is out of range (",results$sim_summary$ad_power[1],")"))
expect_true(
abs(results$sim_summary$ad_power[2] - 0.76) < 0.1,
info = paste0("ad_power[2] is out of range (",results$sim_summary$ad_power[2],")"))
expect_true(
abs(results$sim_summary$ad_power[3] - 0.83) < 0.1,
info = paste0("ad_power[3] is out of range (",results$sim_summary$ad_power[3],")"))
expect_is( results$sim_summary$hypothesis_selection, "numeric")
expect_length( results$sim_summary$hypothesis_selection, 3)
expect_true(
abs(results$sim_summary$hypothesis_selection[1] - 0.28) < 0.1,
info = paste0("hypothesis_selection[1] is out of range (",results$sim_summary$hypothesis_selection[1],")"))
expect_true(
abs(results$sim_summary$hypothesis_selection[2] - 0.13) < 0.1,
info = paste0("hypothesis_selection[2] is out of range (",results$sim_summary$hypothesis_selection[2],")"))
expect_true(
abs(results$sim_summary$hypothesis_selection[3] - 0.57) < 0.1,
info = paste0("hypothesis_selection[3] is out of range (",results$sim_summary$hypothesis_selection[3],")"))
expect_is( results$sim_summary$look_time, "numeric")
expect_length( results$sim_summary$look_time, 4)
expect_true(abs(results$sim_summary$look_time[1] - 0) < 0.1)
expect_true(abs(results$sim_summary$look_time[2] - 0) < 0.1)
expect_true(is.nan(results$sim_summary$look_time[3]))
expect_true(is.nan(results$sim_summary$look_time[4]))
# Check for report generation
ADPopSelReportDoc(results)
})
test_that("Success run ADPopSel with Time-to-event case", {
# Success run
results = ADPopSel(timeToEventCase)
expect_is(results, "ADPopSelResults")
expect_type( results$sim_results, "double")
expect_length(results$sim_results, 15 * timeToEventCase$nsims)
expect_type( results$sim_summary, "list")
expect_true(abs(results$sim_summary$futility - 0.16) < 0.1)
expect_is( results$sim_summary$trad_power, "numeric")
expect_length( results$sim_summary$trad_power, 2)
expect_true(
abs(results$sim_summary$trad_power[1] - 0.66) < 0.1,
info = paste0("trad_power[1] is out of range (",results$sim_summary$trad_power[1],")"))
expect_true(
abs(results$sim_summary$trad_power[2] - 0.58) < 0.1,
info = paste0("trad_power[2] is out of range (",results$sim_summary$trad_power[2],")"))
expect_is( results$sim_summary$ad_power, "numeric")
expect_length( results$sim_summary$ad_power, 3)
expect_true(
abs(results$sim_summary$ad_power[1] - 0.55) < 0.1,
info = paste0("ad_power[1] is out of range (",results$sim_summary$ad_power[1],")"))
expect_true(
abs(results$sim_summary$ad_power[2] - 0.35) < 0.1,
info = paste0("ad_power[2] is out of range (",results$sim_summary$ad_power[2],")"))
expect_true(
abs(results$sim_summary$ad_power[3] - 0.65) < 0.1,
info = paste0("ad_power[3] is out of range (",results$sim_summary$ad_power[3],")"))
expect_is( results$sim_summary$hypothesis_selection, "numeric")
expect_length( results$sim_summary$hypothesis_selection, 3)
expect_true(
abs(results$sim_summary$hypothesis_selection[1] - 0.40) < 0.1,
info = paste0("hypothesis_selection[1] is out of range (",results$sim_summary$hypothesis_selection[1],")"))
expect_true(
abs(results$sim_summary$hypothesis_selection[2] - 0.24) < 0.1,
info = paste0("hypothesis_selection[2] is out of range (",results$sim_summary$hypothesis_selection[2],")"))
expect_true(
abs(results$sim_summary$hypothesis_selection[3] - 0.35) < 0.1,
info = paste0("hypothesis_selection[3] is out of range (",results$sim_summary$hypothesis_selection[3],")"))
expect_is( results$sim_summary$look_time, "numeric")
expect_length( results$sim_summary$look_time, 4)
# Check for report generation
ADPopSelReportDoc(results)
})
context("ADPopSel - Error checks")
test_that("Input parameters errors check ADPopSel", {
# Errors check
expect_error(
ADPopSel(
c("Not a list")
),
info = "Checking for wrong parameters collection type"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
# missing
#endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$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$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for missing endpoint type"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
# wrong
endpoint_type = "WrongType", # normalCase$endpoint_type,
control_mean = normalCase$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$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong endpoint type"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
# wrong
direction = "Wrong",
control_mean = normalCase$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$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong direction"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$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$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for missing information fractions at IA1, IA2, FA"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.8, 0.6, 1.0), # normalCase$info_frac = c(0.4, 0.6, 1.0),
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for error information fractions (IA1 must be < IA2)"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.4, 1.0, 0.9), # normalCase$info_frac = c(0.4, 0.6, 1.0),
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for error information fractions (IA2 must be < FA)"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.4, 0.6, 0.9), # normalCase$info_frac = c(0.4, 0.6, 1.0),
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for error information fractions (FA must be 1)"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.1, 0.4, 0.6, 1.0), # c(0.4, 0.6, 1.0) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong information fractions at IA1, IA2, FA (incorrect value size)"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0, 0.6, 1.0), # c(0.4, 0.6, 1.0) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong information fractions at IA1, IA2, FA (incorrect value <= 0)"
)
expect_error(
ModuleC(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.4, 0.6, 1.1), # c(0.4, 0.6, 1.0) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong information fractions at IA1, IA2, FA (incorrect value > 1)"
)
testParameterErrors = function(params, paramName, paramDesc,
checkMissing = TRUE, checkSize = TRUE, checkMin = NA, checkMax = NA) {
func = ADPopSel
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,
'prevalence',
'Prevalence of biomarker-positive patients',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 0.999)
testParameterErrors(normalCase,
'futility_threshold',
'Futility threshold at IA1',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = -0.001,
checkMax = 0.999)
testParameterErrors(normalCase,
'influence',
'Influence threshold at IA2',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 0.999)
testParameterErrors(normalCase,
'interaction',
'Interaction threshold at IA2',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 1,
checkMax = 2)
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 effects in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = NA,
checkMax = NA)
testParameterErrors(normalCase,
'treatment_mean',
'Mean effects in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = NA,
checkMax = NA)
testParameterErrors(normalCase,
'control_sd',
'Standard deviations in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(normalCase,
'treatment_sd',
'Standard deviations in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(binaryCase,
'control_rate',
'Response rates in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 1)
testParameterErrors(binaryCase,
'treatment_rate',
'Responses rate in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 1)
testParameterErrors(timeToEventCase,
'control_time',
'Median times in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(timeToEventCase,
'treatment_time',
'Median times 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 ADPopSelReport", {
expect_error(
ADPopSelReportDoc(""),
info = "Checking for wrong parameter type for report generator"
)
})
medianasoft/MedianaDesigner documentation built on Aug. 28, 2023, 6:33 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
commonNSim = 50
isTestMultiCore = FALSE
# Normal case parameters
normalCase = list(
# Number of enrolled patients in each trial arm
sample_size = c(120, 120),
# Prevalence
prevalence = 0.4,
# Primary endpoint's type
endpoint_type = "Normal",
# Direction of favorable outcome (Higher or Lower)
# Default: Higher
#direction = "Higher",
# Mean and SD in the control arm
control_mean = c(0, 0),
control_sd = c(1, 1),
# Mean and SD in the treatment arm
treatment_mean = c(0.2, 0.4),
treatment_sd = c(1, 1),
# Information fractions at IA1, IA2, FA
info_frac = c(0.4, 0.6, 1.0),
# Futility threshold
futility_threshold = 0.1,
# Influence threshold for population selection
influence = 0.1,
# Interaction threshold for population selection
interaction = 1.3,
# Dropout rate at the end of the treatment period
dropout_rate = 0.05,
# One-sided Type I error rate
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(120, 120),
# Prevalence of biomarker-positive patients in the overall population
prevalence = 0.4,
# Patient dropout rate
dropout_rate = 0.15,
# Response rate in the control arm (biomarker-negative, biomarker-positive)
control_rate = c(0.1, 0.1),
# Response rate in the treatment arm (biomarker-negative, biomarker-positive)
treatment_rate = c(0.25, 0.4),
# Information fractions at IA1, IA2, FA
info_frac = c(0.4, 0.6, 1),
# Futility threshold for conditional power at IA1
futility_threshold = 0.2,
# Influence threshold at IA2
influence = 0.1,
# Interaction threshold at IA2
interaction = 1.3,
# 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(230, 230),
# Prevalence of biomarker-positive patients in the overall population
prevalence = 0.4,
# Annual patient dropout rate
dropout_rate = 0.05,
# Median times in the control arm (biomarker-negative, biomarker-positive)
control_time = c(7.5, 7.5),
# Median times in the treatment arm (biomarker-negative, biomarker-positive)
treatment_time = c(9.5, 11),
# Information fractions at IA1, IA2, FA
info_frac = c(0.4, 0.6, 1),
# Target event count at FA (overall, biomarker-positive)
event_count = c(300, 150),
# Futility threshold for conditional power at IA1
futility_threshold = 0.1,
# Influence threshold at IA2
influence = 0.1,
# Interaction threshold at IA2
interaction = 1.3,
# Enrollment period
enrollment_period = 12,
# Median enrollment time
enrollment_parameter = 8,
# One-sided alpha level
alpha = 0.025,
# Number of simulations
nsims = commonNSim
)
context("ADPopSel - Success runs")
checkExpectationsForNormalCase = function(results) {
expect_s3_class(results, "ADPopSelResults")
expect_type( results$sim_results, "double")
expect_length(results$sim_results, 15 * results$parameters$nsims)
sim_summary = results$sim_summary
# print(sim_summary)
expect_type(sim_summary, "list")
expect_equal(sim_summary$futility, 0.209, tolerance=0.1)
expect_is( sim_summary$trad_power, "numeric")
expect_length(sim_summary$trad_power, 2)
expect_equal( unname(sim_summary$trad_power[1]), 0.541, tolerance=0.1)
expect_equal( unname(sim_summary$trad_power[2]), 0.463, tolerance=0.1)
expect_is( sim_summary$ad_power, "numeric")
expect_length(sim_summary$ad_power, 3)
expect_equal( unname(sim_summary$ad_power[1]), 0.458, tolerance=0.1)
expect_equal( unname(sim_summary$ad_power[2]), 0.330, tolerance=0.1)
expect_equal( unname(sim_summary$ad_power[3]), 0.483, tolerance=0.1)
expect_is( sim_summary$hypothesis_selection, "numeric")
expect_length(sim_summary$hypothesis_selection, 3)
expect_equal( unname(sim_summary$hypothesis_selection[1]), 0.332, tolerance=0.1)
expect_equal( unname(sim_summary$hypothesis_selection[2]), 0.323, tolerance=0.1)
expect_equal( unname(sim_summary$hypothesis_selection[3]), 0.345, tolerance=0.1)
expect_is( sim_summary$look_time, "numeric")
expect_length(sim_summary$look_time, 4)
expect_equal( unname(sim_summary$look_time[1]), 0, tolerance=0.1)
expect_equal( unname(sim_summary$look_time[2]), 0, tolerance=0.1)
expect_true(is.nan(results$sim_summary$look_time[3]))
expect_true(is.nan(results$sim_summary$look_time[4]))
}
test_that("Success run ADPopSel with Normal case (single core)", {
# Success run
results = ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$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$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
# missing, use default 0
#dropout_rate = normalCase$dropout_rate,
# missing, use default 0.025
#alpha = normalCase$alpha,
# missing, use default 1000
#nsims = 1000 # normalCase$nsims
# Run once with random seed
random_seed = 49283
)
)
checkExpectationsForNormalCase(results)
# Check for report generation
ADPopSelReportDoc(results)
GenerateReport(results, tempfile(fileext = ".docx"))
})
if (isTestMultiCore) {
test_that("Success run ADPopSel with Normal case (two cores)", {
# Success run
params_for_run = normalCase
params_for_run$ncores = 2
results = ADPopSel(params_for_run)
checkExpectationsForNormalCase(results)
})
}
test_that("Success run ADPopSel with direction Lower", {
# Check for non-fail Lower direction success
# Success run
results = ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
direction = "Lower",
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,
influence = normalCase$influence,
interaction = normalCase$interaction#,
# missing, use default 0
#dropout_rate = normalCase$dropout_rate,
# missing, use default 0.025
#alpha = normalCase$alpha,
# missing, use default 1000
#nsims = 1000 # normalCase$nsims
)
)
expect_is(results, "ADPopSelResults")
ADPopSelReportDoc(results)
})
test_that("Success run ADPopSel with direction Lower & endPoint=3", {
# Check for non-fail Lower direction success
# Success run
results = ADPopSel(
list( # timeToEventCase
endpoint_type = timeToEventCase$endpoint_type,
direction = "Lower",
sample_size = timeToEventCase$sample_size,
prevalence = timeToEventCase$prevalence,
dropout_rate = timeToEventCase$dropout_rate,
control_time = timeToEventCase$control_time,
treatment_time = timeToEventCase$treatment_time,
info_frac = timeToEventCase$info_frac,
event_count = timeToEventCase$event_count,
futility_threshold = timeToEventCase$futility_threshold,
influence = timeToEventCase$influence,
interaction = timeToEventCase$interaction,
enrollment_period = timeToEventCase$enrollment_period,
enrollment_parameter = timeToEventCase$enrollment_parameter,
alpha = timeToEventCase$alpha,
nsims = timeToEventCase$nsims
)
)
expect_is(results, "ADPopSelResults")
ADPopSelReportDoc(results)
})
test_that("Success run ADPopSel with Binary case", {
# Success run
results = ADPopSel(binaryCase)
expect_is(results, "ADPopSelResults")
expect_type( results$sim_results, "double")
expect_length(results$sim_results, 15 * binaryCase$nsims)
expect_type( results$sim_summary, "list")
expect_true(abs(results$sim_summary$futility - 0.05) < 0.1)
expect_is( results$sim_summary$trad_power, "numeric")
expect_length( results$sim_summary$trad_power, 2)
expect_true(
abs(results$sim_summary$trad_power[1] - 0.92) < 0.1,
info = paste0("trad_power[1] is out of range (",results$sim_summary$trad_power[1],")"))
expect_true(
abs(results$sim_summary$trad_power[2] - 0.86) < 0.1,
info = paste0("trad_power[2] is out of range (",results$sim_summary$trad_power[2],")"))
expect_is( results$sim_summary$ad_power, "numeric")
expect_length( results$sim_summary$ad_power, 3)
expect_true(
abs(results$sim_summary$ad_power[1] - 0.82) < 0.1,
info = paste0("ad_power[1] is out of range (",results$sim_summary$ad_power[1],")"))
expect_true(
abs(results$sim_summary$ad_power[2] - 0.76) < 0.1,
info = paste0("ad_power[2] is out of range (",results$sim_summary$ad_power[2],")"))
expect_true(
abs(results$sim_summary$ad_power[3] - 0.83) < 0.1,
info = paste0("ad_power[3] is out of range (",results$sim_summary$ad_power[3],")"))
expect_is( results$sim_summary$hypothesis_selection, "numeric")
expect_length( results$sim_summary$hypothesis_selection, 3)
expect_true(
abs(results$sim_summary$hypothesis_selection[1] - 0.28) < 0.1,
info = paste0("hypothesis_selection[1] is out of range (",results$sim_summary$hypothesis_selection[1],")"))
expect_true(
abs(results$sim_summary$hypothesis_selection[2] - 0.13) < 0.1,
info = paste0("hypothesis_selection[2] is out of range (",results$sim_summary$hypothesis_selection[2],")"))
expect_true(
abs(results$sim_summary$hypothesis_selection[3] - 0.57) < 0.1,
info = paste0("hypothesis_selection[3] is out of range (",results$sim_summary$hypothesis_selection[3],")"))
expect_is( results$sim_summary$look_time, "numeric")
expect_length( results$sim_summary$look_time, 4)
expect_true(abs(results$sim_summary$look_time[1] - 0) < 0.1)
expect_true(abs(results$sim_summary$look_time[2] - 0) < 0.1)
expect_true(is.nan(results$sim_summary$look_time[3]))
expect_true(is.nan(results$sim_summary$look_time[4]))
# Check for report generation
ADPopSelReportDoc(results)
})
test_that("Success run ADPopSel with Time-to-event case", {
# Success run
results = ADPopSel(timeToEventCase)
expect_is(results, "ADPopSelResults")
expect_type( results$sim_results, "double")
expect_length(results$sim_results, 15 * timeToEventCase$nsims)
expect_type( results$sim_summary, "list")
expect_true(abs(results$sim_summary$futility - 0.16) < 0.1)
expect_is( results$sim_summary$trad_power, "numeric")
expect_length( results$sim_summary$trad_power, 2)
expect_true(
abs(results$sim_summary$trad_power[1] - 0.66) < 0.1,
info = paste0("trad_power[1] is out of range (",results$sim_summary$trad_power[1],")"))
expect_true(
abs(results$sim_summary$trad_power[2] - 0.58) < 0.1,
info = paste0("trad_power[2] is out of range (",results$sim_summary$trad_power[2],")"))
expect_is( results$sim_summary$ad_power, "numeric")
expect_length( results$sim_summary$ad_power, 3)
expect_true(
abs(results$sim_summary$ad_power[1] - 0.55) < 0.1,
info = paste0("ad_power[1] is out of range (",results$sim_summary$ad_power[1],")"))
expect_true(
abs(results$sim_summary$ad_power[2] - 0.35) < 0.1,
info = paste0("ad_power[2] is out of range (",results$sim_summary$ad_power[2],")"))
expect_true(
abs(results$sim_summary$ad_power[3] - 0.65) < 0.1,
info = paste0("ad_power[3] is out of range (",results$sim_summary$ad_power[3],")"))
expect_is( results$sim_summary$hypothesis_selection, "numeric")
expect_length( results$sim_summary$hypothesis_selection, 3)
expect_true(
abs(results$sim_summary$hypothesis_selection[1] - 0.40) < 0.1,
info = paste0("hypothesis_selection[1] is out of range (",results$sim_summary$hypothesis_selection[1],")"))
expect_true(
abs(results$sim_summary$hypothesis_selection[2] - 0.24) < 0.1,
info = paste0("hypothesis_selection[2] is out of range (",results$sim_summary$hypothesis_selection[2],")"))
expect_true(
abs(results$sim_summary$hypothesis_selection[3] - 0.35) < 0.1,
info = paste0("hypothesis_selection[3] is out of range (",results$sim_summary$hypothesis_selection[3],")"))
expect_is( results$sim_summary$look_time, "numeric")
expect_length( results$sim_summary$look_time, 4)
# Check for report generation
ADPopSelReportDoc(results)
})
context("ADPopSel - Error checks")
test_that("Input parameters errors check ADPopSel", {
# Errors check
expect_error(
ADPopSel(
c("Not a list")
),
info = "Checking for wrong parameters collection type"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
# missing
#endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$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$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for missing endpoint type"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
# wrong
endpoint_type = "WrongType", # normalCase$endpoint_type,
control_mean = normalCase$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$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong endpoint type"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
# wrong
direction = "Wrong",
control_mean = normalCase$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$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong direction"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$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$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for missing information fractions at IA1, IA2, FA"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.8, 0.6, 1.0), # normalCase$info_frac = c(0.4, 0.6, 1.0),
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for error information fractions (IA1 must be < IA2)"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.4, 1.0, 0.9), # normalCase$info_frac = c(0.4, 0.6, 1.0),
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for error information fractions (IA2 must be < FA)"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.4, 0.6, 0.9), # normalCase$info_frac = c(0.4, 0.6, 1.0),
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for error information fractions (FA must be 1)"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.1, 0.4, 0.6, 1.0), # c(0.4, 0.6, 1.0) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong information fractions at IA1, IA2, FA (incorrect value size)"
)
expect_error(
ADPopSel(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0, 0.6, 1.0), # c(0.4, 0.6, 1.0) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong information fractions at IA1, IA2, FA (incorrect value <= 0)"
)
expect_error(
ModuleC(
list(
sample_size = normalCase$sample_size,
prevalence = normalCase$prevalence,
endpoint_type = normalCase$endpoint_type,
control_mean = normalCase$control_mean,
control_sd = normalCase$control_sd,
treatment_mean = normalCase$treatment_mean,
treatment_sd = normalCase$treatment_sd,
# wrong
info_frac = c(0.4, 0.6, 1.1), # c(0.4, 0.6, 1.0) = normalCase$info_frac,
futility_threshold = normalCase$futility_threshold,
influence = normalCase$influence,
interaction = normalCase$interaction,
dropout_rate = normalCase$dropout_rate,
alpha = normalCase$alpha,
nsims = normalCase$nsims
)
),
info = "Checking for wrong information fractions at IA1, IA2, FA (incorrect value > 1)"
)
testParameterErrors = function(params, paramName, paramDesc,
checkMissing = TRUE, checkSize = TRUE, checkMin = NA, checkMax = NA) {
func = ADPopSel
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,
'prevalence',
'Prevalence of biomarker-positive patients',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 0.999)
testParameterErrors(normalCase,
'futility_threshold',
'Futility threshold at IA1',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = -0.001,
checkMax = 0.999)
testParameterErrors(normalCase,
'influence',
'Influence threshold at IA2',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 0.999)
testParameterErrors(normalCase,
'interaction',
'Interaction threshold at IA2',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 1,
checkMax = 2)
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 effects in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = NA,
checkMax = NA)
testParameterErrors(normalCase,
'treatment_mean',
'Mean effects in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = NA,
checkMax = NA)
testParameterErrors(normalCase,
'control_sd',
'Standard deviations in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(normalCase,
'treatment_sd',
'Standard deviations in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(binaryCase,
'control_rate',
'Response rates in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 1)
testParameterErrors(binaryCase,
'treatment_rate',
'Responses rate in the treatment arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = 1)
testParameterErrors(timeToEventCase,
'control_time',
'Median times in the control arm',
checkMissing = TRUE,
checkSize = TRUE,
checkMin = 0,
checkMax = NA)
testParameterErrors(timeToEventCase,
'treatment_time',
'Median times 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 ADPopSelReport", {
expect_error(
ADPopSelReportDoc(""),
info = "Checking for wrong parameter type for report generator"
)
})
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.