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# Helper functions used by test-independent-AHR.R | ||
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test_ahr <- function() { | ||
load("fixtures/simulation_test_data.Rdata") | ||
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enroll_rate <- define_enroll_rate( | ||
duration = c(2, 2, 10), | ||
rate = c(3, 6, 9) | ||
) | ||
fail_rate <- define_fail_rate( | ||
stratum = "All", | ||
duration = c(3, 100), | ||
fail_rate = log(2) / c(9, 18), | ||
hr = c(.9, .6), | ||
dropout_rate = rep(.001, 2) | ||
) | ||
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list( | ||
"simulation_ahr1" = simulation_AHR1, | ||
"simulation_ahr2" = simulation_AHR2, | ||
"simulation_ahr3" = simulation_AHR3, | ||
"enroll_rate" = enroll_rate, | ||
"fail_rate" = fail_rate | ||
) | ||
} |
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# Helper functions used by test-independent-expected_accrual.R | ||
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test_eAccrual <- function(x, enroll_rate) { | ||
boundary <- cumsum(enroll_rate$duration) | ||
rate <- enroll_rate$rate | ||
xvals <- unique(c(x, boundary)) | ||
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eAc2 <- numeric(length(xvals)) | ||
for (t in seq_along(xvals)) { | ||
val <- xvals[t] | ||
if (val <= boundary[1]) { | ||
eAc2[t] <- val * rate[1] | ||
} else if (val <= boundary[2]) { | ||
eAc2[t] <- boundary[1] * rate[1] + (val - boundary[1]) * rate[2] | ||
} else if (val <= boundary[3]) { | ||
eAc2[t] <- boundary[1] * rate[1] + | ||
(boundary[2] - boundary[1]) * rate[2] + (val - boundary[2]) * rate[3] | ||
} else { | ||
eAc2[t] <- boundary[1] * rate[1] + | ||
(boundary[2] - boundary[1]) * rate[2] + (boundary[3] - boundary[2]) * rate[3] | ||
} | ||
} | ||
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ind <- !is.na(match(xvals, x)) | ||
return(eAc2[ind]) | ||
} |
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# Helper functions used by test-independent-expected_event.R | ||
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n_event <- function(failRates, followup) { | ||
failduration <- failRates$duration | ||
failtime <- cumsum(failduration) | ||
failRate <- failRates$failRate | ||
dropoutRate <- failRates$dropoutRate | ||
lamda <- failRate + dropoutRate | ||
lamda1 <- c(lamda, dplyr::last(lamda)) | ||
failRate1 <- c(failRate, dplyr::last(failRate)) | ||
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failtimeend <- c(0, failtime[failtime < followup], followup) | ||
failtimeend1 <- c(failtime[failtime < followup], followup) | ||
lamda2 <- lamda1[c(1:(length(failtimeend) - 1))] | ||
failRate2 <- failRate1[c(1:(length(failtimeend) - 1))] | ||
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failduration <- diff(failtimeend) | ||
failduration2 <- followup - failtimeend1 | ||
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fail <- lamda2 * failduration | ||
sumfail <- cumsum(fail) | ||
Bi1 <- c(1, exp(-sumfail)) | ||
diffbi <- diff(Bi1) | ||
Bi <- Bi1[c(1:(length(Bi1) - 1))] | ||
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totalevent <- diffbi * (1 / lamda2 - failduration2) + Bi * failduration | ||
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failevent <- totalevent * (failRate2 / lamda2) | ||
return(sum(failevent)) | ||
} | ||
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test_expected_event <- function(enrollRates, failRates, totalDuration) { | ||
enrolltime <- c(0, cumsum(enrollRates$duration)) | ||
Event <- 0 | ||
for (i in seq_along(enrollRates$duration)) { | ||
enrollmentstart <- 0 | ||
enrollmentend <- enrollRates$duration[i] | ||
enrollrate <- enrollRates$rate[i] | ||
followup <- totalDuration - enrolltime[i] | ||
nEventnum <- 0 | ||
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if (followup > 0 && followup <= enrollmentend) { | ||
nEventnum <- n_event(failRates, followup) * enrollrate | ||
} else if (followup > 0 && followup > enrollmentend) { | ||
nEventnum <- (n_event(failRates, followup) - n_event(failRates, followup - enrollmentend)) * enrollrate | ||
} else { | ||
nEventnum <- 0 | ||
} | ||
Event <- Event + nEventnum | ||
} | ||
return(Event) | ||
} | ||
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params_expected_event <- function() { | ||
enroll_rate <- define_enroll_rate( | ||
duration = c(50), | ||
rate = c(10) | ||
) | ||
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fail_rate <- define_fail_rate( | ||
duration = c(10, 20, 10), | ||
fail_rate = log(2) / c(5, 10, 5), | ||
dropout_rate = c(0.1, 0.2, 0), | ||
hr = 1 | ||
) | ||
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fail_rate$failRate <- fail_rate$fail_rate | ||
fail_rate$dropoutRate <- fail_rate$dropout_rate | ||
failRates <- fail_rate | ||
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total_duration <- 5 | ||
simple <- TRUE | ||
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list( | ||
"enroll_rate" = enroll_rate, | ||
"fail_rate" = fail_rate, | ||
"failRates" = failRates, | ||
"total_duration" = total_duration, | ||
"simple" = simple | ||
) | ||
} |
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# Helper functions used by test-independent-expected_time.R | ||
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test_expected_time <- function() { | ||
enroll_rate <- define_enroll_rate( | ||
duration = c(2, 2, 10), | ||
rate = c(3, 6, 9) * 5 | ||
) | ||
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fail_rate <- define_fail_rate( | ||
duration = c(3, 100), | ||
fail_rate = log(2) / c(9, 18), | ||
dropout_rate = rep(.001, 2), | ||
hr = c(.9, .6) | ||
) | ||
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target_event <- 150 | ||
interval <- c(.01, 100) | ||
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t1 <- expected_time( | ||
enroll_rate = enroll_rate, | ||
fail_rate = fail_rate, | ||
target_event = target_event, | ||
interval = interval | ||
) | ||
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list("enroll_rate" = enroll_rate, "fail_rate" = fail_rate, "t1" = t1) | ||
} |
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# Helper functions used by test-independent-fixed_design.R | ||
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test_fixed_design <- function() { | ||
# Enrollment rate | ||
enroll_rate <- define_enroll_rate( | ||
duration = 18, | ||
rate = 20 | ||
) | ||
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# Failure rates | ||
fail_rate <- define_fail_rate( | ||
duration = c(4, 100), | ||
fail_rate = log(2) / 12, | ||
dropout_rate = .001, | ||
hr = c(1, .6) | ||
) | ||
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# Study duration in months | ||
study_duration <- 36 | ||
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# Experimental / Control randomization ratio | ||
ratio <- 1 | ||
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list( | ||
"enroll_rate" = enroll_rate, | ||
"fail_rate" = fail_rate, | ||
"study_duration" = study_duration, | ||
"ratio" = ratio | ||
) | ||
} |
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# Helper functions used by test-independent-gs_design_combo.R | ||
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test_gs_design_combo <- function() { | ||
load("fixtures/sim_gsd_pMaxCombo_exp1_H0_test.Rdata") | ||
load("fixtures/sim_gsd_pMaxCombo_exp1_H1_test.Rdata") | ||
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ratio <- 1 | ||
algorithm <- mvtnorm::GenzBretz(maxpts = 1e5, abseps = 1e-5) | ||
alpha <- 0.025 | ||
beta <- 0.2 | ||
enroll_rate <- define_enroll_rate(duration = 12, rate = 500 / 12) | ||
fail_rate <- define_fail_rate( | ||
duration = c(4, 100), | ||
fail_rate = log(2) / 15, # Median survival 15 month | ||
dropout_rate = 0.001, | ||
hr = c(1, .6) | ||
) | ||
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fh_test <- rbind( | ||
data.frame( | ||
rho = 0, | ||
gamma = 0, | ||
tau = -1, | ||
test = 1, | ||
analysis = 1:3, | ||
analysis_time = c(12, 24, 36) | ||
), | ||
data.frame( | ||
rho = c(0, 0.5), | ||
gamma = 0.5, | ||
tau = -1, | ||
test = 2:3, | ||
analysis = 3, | ||
analysis_time = 36 | ||
) | ||
) | ||
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x <- gsDesign::gsSurv( | ||
k = 3, | ||
test.type = 4, | ||
alpha = 0.025, | ||
beta = 0.2, | ||
astar = 0, | ||
timing = c(1), | ||
sfu = gsDesign::sfLDOF, | ||
sfupar = c(0), | ||
sfl = gsDesign::sfLDOF, | ||
sflpar = c(0), | ||
lambdaC = c(0.1), | ||
hr = 0.6, | ||
hr0 = 1, | ||
eta = 0.01, | ||
gamma = c(10), | ||
R = c(12), | ||
S = NULL, | ||
T = 36, | ||
minfup = 24, | ||
ratio = 1 | ||
) | ||
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# User-defined boundary | ||
gs_design_combo_test1 <- gs_design_combo( | ||
enroll_rate = enroll_rate, | ||
fail_rate = fail_rate, | ||
fh_test = fh_test, | ||
alpha = alpha, | ||
beta = beta, | ||
ratio = 1, | ||
binding = FALSE, # test.type = 4 non-binding futility bound | ||
upar = x$upper$bound, | ||
lpar = x$lower$bound | ||
) | ||
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# Boundary derived by spending function testing | ||
gs_design_combo_test2 <- gs_design_combo( | ||
enroll_rate = enroll_rate, | ||
fail_rate = fail_rate, | ||
fh_test = fh_test, | ||
alpha = 0.025, | ||
beta = 0.2, | ||
ratio = 1, | ||
binding = FALSE, # test.type = 4 non-binding futility bound | ||
upper = gs_spending_combo, | ||
upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025), # alpha spending | ||
lower = gs_spending_combo, | ||
lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2), # beta spending | ||
) | ||
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list( | ||
"alpha" = alpha, | ||
"beta" = beta, | ||
"enroll_rate" = enroll_rate, | ||
"fail_rate" = fail_rate, | ||
"fh_test" = fh_test, | ||
"gs_design_combo_test2" = gs_design_combo_test2 | ||
) | ||
} |
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# Helper functions used by test-independent-gs_design_npe.R | ||
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# Parameters used repeatedly | ||
params_gs_design_npe <- list( | ||
K = 3, | ||
timing = c(.45, .8, 1), | ||
sfu = gsDesign::sfPower, | ||
sfupar = 4, | ||
sfl = gsDesign::sfHSD, | ||
sflpar = 2, | ||
delta = .2, | ||
alpha = .02, | ||
beta = .15 | ||
) |
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# Helper functions used by test-independent-gs_info_ahr.R | ||
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test_gs_info_ahr <- function() { | ||
enroll_rate <- define_enroll_rate( | ||
duration = c(2, 2, 10), | ||
rate = c(3, 6, 9) | ||
) | ||
fail_rate <- define_fail_rate( | ||
duration = c(3, 100), | ||
fail_rate = log(2) / c(9, 18), | ||
hr = c(0.9, 0.6), | ||
dropout_rate = 0.001 | ||
) | ||
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list("enroll_rate" = enroll_rate, "fail_rate" = fail_rate) | ||
} |
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# Helper functions used by test-independent-gs_info_combo.R | ||
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test_gs_info_combo <- function() { | ||
rho <- c(1, 1, 0, 0) | ||
gamma <- c(0, 1, 0, 1) | ||
tau <- c(-1, -1, -1, -1) | ||
enroll_rate <- define_enroll_rate( | ||
duration = c(2, 2, 30), | ||
rate = c(3, 6, 9) | ||
) | ||
fail_rate <- define_fail_rate( | ||
duration = c(3, 100), | ||
fail_rate = log(2) / c(9, 18), | ||
dropout_rate = rep(.001, 2), | ||
hr = c(.9, .6) | ||
) | ||
info_combo <- gsDesign2::gs_info_combo( | ||
enroll_rate = enroll_rate, | ||
fail_rate = fail_rate, | ||
ratio = 1, # Experimental:Control randomization ratio | ||
event = NULL, # Events at analyses | ||
analysis_time = 30, # Times of analyses | ||
rho = rho, | ||
gamma = gamma, | ||
tau = rep(-1, length(rho)), | ||
approx = "asymptotic" | ||
) | ||
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list( | ||
"rho" = rho, | ||
"gamma" = gamma, | ||
"tau" = tau, | ||
"enroll_rate" = enroll_rate, | ||
"fail_rate" = fail_rate, | ||
"info_combo" = info_combo | ||
) | ||
} |
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