validation/test_code/02_BiasAdjustmentTestCode.R
In Sterniii3/drugdevelopR: Utility-Based Optimal Phase II/III Drug Development Planning
#' @editor Lukas D Sauer
#' @editDate 2022-12-07
test_that("02.01", {
# Adjustment method "additive" for time-to-event endpoints
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "additive",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = NULL, lambdamax = NULL, steplambda = NULL
)
expect_equal(res$n2, 122)
expect_equal(res$n3, 200)
expect_equal(res$n, 322)
expect_equal(res$u, 78, tolerance = 0.005)
expect_equal(res$HRgo, 0.78)
expect_equal(res$d2, 85)
expect_equal(res$d3, 140)
expect_equal(res$d, 225)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-07
test_that("02.02", {
# Adjustment method "multiplicative" for time-to-event endpoints
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "multiplicative",
alphaCImin = NULL, alphaCImax = NULL, stepalphaCI = NULL,
lambdamin = 0.5, lambdamax = 1, steplambda = 0.05
)
expect_equal(res$n2, 136)
expect_equal(res$n3, 244)
expect_equal(res$n, 380)
expect_equal(res$u, 99, tolerance = 0.005)
expect_equal(res$HRgo, 0.76)
expect_equal(res$pgo, 0.38)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-07
test_that("02.03", {
# Adjustment method "both" for time-to-event endpoints
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "both",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = 0.5, lambdamax = 1, steplambda = 0.05,
N = 350
)
expect_equal(res$Method, c("multipl.", "add."))
# Additive method
expect_equal(res[2,]$n2, 122)
expect_equal(res[2,]$n3, 200)
expect_equal(res[2,]$n, 322)
expect_equal(res[2,]$u, 78, tolerance = 0.005)
expect_equal(res[2,]$HRgo, 0.78)
expect_equal(res[2,]$d2, 85)
expect_equal(res[2,]$d3, 140)
expect_equal(res[2,]$d, 225)
# Multiplicative method
expect_equal(res[1,]$n2, 100)
expect_equal(res[1,]$n3, 240)
expect_equal(res[1,]$n, 340)
expect_equal(res[1,]$u, 98, tolerance = 0.01)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-07
test_that("02.04", {
# Adjustment method "all" for time-to-event endpoints
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "all",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = 0.5, lambdamax = 1, steplambda = 0.05,
N = 350
)
expect_equal(res$Method, c("multipl.", "add.", "multipl2.", "add2."))
# Additive
expect_equal(res[2,]$n2, 122)
expect_equal(res[2,]$n3, 200)
expect_equal(res[2,]$n, 322)
expect_equal(res[2,]$u, 78, tolerance = 0.005)
expect_equal(res[2,]$HRgo, 0.78)
expect_equal(res[2,]$d2, 85)
expect_equal(res[2,]$d3, 140)
expect_equal(res[2,]$d, 225)
# Multiplicative
expect_equal(res[1,]$n2, 100)
expect_equal(res[1,]$n3, 240)
expect_equal(res[1,]$n, 340)
expect_equal(res[1,]$u, 98, tolerance = 0.01)
# Multiplicative 2
expect_equal(res[3,]$n2, 108)
expect_equal(res[3,]$n3, 200)
expect_equal(res[3,]$n, 308)
expect_equal(res[3,]$u, 96.69, tolerance = 0.0001)
expect_equal(res[3,]$Adj, 0.75)
# Additive 2
expect_equal(res[4,]$n2, 136)
expect_equal(res[4,]$n3, 206)
expect_equal(res[4,]$n, 342)
expect_equal(res[4,]$u, 77.40, tolerance = 0.0001)
expect_equal(res[4,]$Adj, 0.475)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-13
test_that("02.05", {
# Without and with cost constraint
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = TRUE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "additive",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = NULL, lambdamax = NULL, steplambda = NULL
)
expect_equal(res$n2, 144)
expect_equal(res$n3, 478)
expect_equal(res$u, 865.02)
expect_equal(res$Adj, 0.5)
expect_equal(res$K2, 208)
expect_equal(res$K3, 608)
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = TRUE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "additive",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = NULL, lambdamax = NULL, steplambda = NULL,
K = 400
)
expect_equal(res$n2, 44)
expect_equal(res$n3, 172)
expect_equal(res$u, 474.18)
expect_equal(res$Adj, 0.475)
expect_equal(res$K2, 133)
expect_equal(res$K3, 263)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-14
test_that("02.06", {
# With and without probability constraint
# for time-to-event endpoints
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = TRUE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "additive",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = NULL, lambdamax = NULL, steplambda = NULL
)
expect_equal(res$n2, 144)
expect_equal(res$n3, 478)
expect_equal(res$u, 865.02)
expect_equal(res$Adj, 0.5)
expect_equal(res$sProg, 0.68)
expect_equal(res$sProg1, 0.07)
expect_equal(res$sProg2, 0.21)
expect_equal(res$sProg3, 0.39)
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = TRUE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "additive",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = NULL, lambdamax = NULL, steplambda = NULL,
S = 0.7
)
expect_equal(res$n2, 144)
expect_equal(res$n3, 552)
expect_equal(res$u, 857.77)
expect_equal(res$Adj, 0.5)
expect_equal(res$sProg, 0.7)
expect_equal(res$sProg1, 0.07)
expect_equal(res$sProg2, 0.21)
expect_equal(res$sProg3, 0.42)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-14
test_that("02.07", {
# No bias adjustment is equal to the basic setting
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "both",
alphaCImin = 0.5, alphaCImax = 0.5, stepalphaCI = 1,
lambdamin = 1, lambdamax = 1, steplambda = 1
)
expect_equal(res[1,]$n2, 122)
expect_equal(res[1,]$n3, 210)
expect_equal(res[1,]$n, 332)
expect_equal(res[1,]$u, 75.8, tolerance = 0.005)
expect_equal(res[2,]$n2, 122)
expect_equal(res[2,]$n3, 210)
expect_equal(res[2,]$n, 332)
expect_equal(res[2,]$u, 75.8, tolerance = 0.005)
res <- optimal_tte(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
)
expect_equal(res$n2, 122)
expect_equal(res$n3, 210)
expect_equal(res$n, 332)
expect_equal(res$u, 75.8, tolerance = 0.005)
})
# #' @editor Lukas D Sauer
# #' @editDate 2022-12-14
# test_that("02.08", {
# # Parallel computing works
# start_time_3 = Sys.time()
#
# res <- optimal_bias(alpha = 0.025,
# beta = 0.1,
# hr1 = 0.69, hr2 = 0.88,
# xi2 = 0.7, xi3 = 0.7,
# d2min = 20, d2max = 100, stepd2 = 5,
# hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
# steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
# b1 = 1000, b2 = 2000, b3 = 3000,
# num_cl = 12,
# c02 = 100, c03 = 150, # fixed cost
# c2 = 0.75, c3 = 1, # variable per patient cost
# fixed = FALSE,
# w = 0.3,
# id1 = 210, id2 = 420,
# adj = "additive",
# alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
# lambdamin = NULL, lambdamax = NULL, steplambda = NULL
# )
#
# end_time_3 = Sys.time()
# time_elapsed_num_cl_3 = end_time_3 - start_time_3
# start_time_1 = Sys.time()
#
# res <- optimal_bias(alpha = 0.025,
# beta = 0.1,
# hr1 = 0.69, hr2 = 0.88,
# xi2 = 0.7, xi3 = 0.7,
# d2min = 20, d2max = 100, stepd2 = 5,
# hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
# steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
# b1 = 1000, b2 = 2000, b3 = 3000,
# num_cl = 6,
# c02 = 100, c03 = 150, # fixed cost
# c2 = 0.75, c3 = 1, # variable per patient cost
# fixed = FALSE,
# w = 0.3,
# id1 = 210, id2 = 420,
# adj = "additive",
# alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
# lambdamin = NULL, lambdamax = NULL, steplambda = NULL
# )
#
# end_time_1 = Sys.time()
# time_elapsed_num_cl_1 = end_time_1 - start_time_1
# expect_true(time_elapsed_num_cl_1 > time_elapsed_num_cl_3)
# })
#' @editor Lukas D Sauer
#' @editDate 2022-12-14
test_that("02.09", {
# Bias adjustment for normally distributed endpoints
res <- optimal_bias_normal(
alpha = 0.05,
beta = 0.1,
Delta1 = 0.625, Delta2 = 0.325,
n2min = 20, n2max = 400, stepn2 = 4,
kappamin = 0.02, kappamax = 0.4, stepkappa = 0.02,
steps1 = 0, stepm1 = 0.5, stepl1 = 0.8,
b1 = 3000, b2 = 8000, b3 = 10000,
num_cl = 12,
c02 = 15, c03 = 20,
c2 = 0.675, c3 = 0.72,
fixed = FALSE,
w = 0.5,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75,
adj = "multiplicative",
lambdamin = 0.7, lambdamax = 0.9, steplambda = 0.01
)
expect_equal(res$n2, 192)
expect_equal(res$n3, 474)
expect_equal(res$n, 666)
expect_equal(res$Kappa, 0.12)
expect_equal(res$u, 2899.32, tolerance = 0.005)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-14
test_that("02.10", {
# Bias adjustment for normally distributed endpoints
# with fixed prior treatment effect and all adjustment
# methods
res <- optimal_bias_normal(
alpha = 0.05,
beta = 0.1,
Delta1 = 0.625, Delta2 = 0.325,
n2min = 20, n2max = 400, stepn2 = 4,
kappamin = 0.02, kappamax = 0.4, stepkappa = 0.02,
steps1 = 0, stepm1 = 0.5, stepl1 = 0.8,
b1 = 3000, b2 = 8000, b3 = 10000,
num_cl = 12,
c02 = 15, c03 = 20,
c2 = 0.675, c3 = 0.72,
fixed = TRUE,
w = 0.5,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75,
adj = "all",
alphaCImin = 0.25, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = 0.7, lambdamax = 0.9, steplambda = 0.01
)
expect_equal(res$Method, c("multipl.", "add.", "multipl2.", "add2."))
# Multiplicative
expect_equal(res[1,]$n2, 88)
expect_equal(res[1,]$n3, 310)
expect_equal(res[1,]$n, 398)
expect_equal(res[1,]$u, 3861.76, tolerance = 0.0001)
expect_equal(res[1,]$Adj, 0.7, tolerance = 0.0001)
# Additive
expect_equal(res[2,]$n2, 96)
expect_equal(res[2,]$n3, 306)
expect_equal(res[2,]$n, 402)
expect_equal(res[2,]$u, 3631.51, tolerance = 0.0001)
expect_equal(res[2,]$Adj, 0.25, tolerance = 0.0001)
# Multiplicative 2
expect_equal(res[3,]$n2, 88)
expect_equal(res[3,]$n3, 306)
expect_equal(res[3,]$n, 394)
expect_equal(res[3,]$u, 3860.12, tolerance = 0.0001)
expect_equal(res[3,]$Adj, 0.7)
# Additive 2
expect_equal(res[4,]$n2, 96)
expect_equal(res[4,]$n3, 312)
expect_equal(res[4,]$n, 408)
expect_equal(res[4,]$u, 3631.28, tolerance = 0.0001)
expect_equal(res[4,]$Adj, 0.25)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-20
test_that("02.11", {
res <- optimal_bias_binary(
alpha = 0.025,
beta = 0.1,
p0 = 0.6, p11 = 0.3, p12= 0.5,
n2min = 10, n2max = 500, stepn2 = 2,
rrgomin = 0.7, rrgomax = 0.9, steprrgo = 0.01,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150,
c2 = 0.75, c3 = 1,
fixed = FALSE,
w = 0.3,
in1 = 30, in2 = 60,
adj = "additive",
alphaCImin = 0.1, alphaCImax = 0.5, stepalphaCI = 0.025,
)
expect_equal(res$n2, 166)
expect_equal(res$n3, 264)
expect_equal(res$n, 430)
expect_equal(res$RRgo, 0.82)
expect_equal(res$u, 605.91, tolerance = 0.005)
expect_equal(res$Adj, 0.275)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-20
test_that("02.12", {
res <- optimal_bias_binary(
alpha = 0.025,
beta = 0.1,
p0 = 0.6, p11 = 0.3, p12= 0.5,
n2min = 10, n2max = 500, stepn2 = 2,
rrgomin = 0.7, rrgomax = 0.9, steprrgo = 0.01,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150,
c2 = 0.75, c3 = 1,
fixed = TRUE,
w = 0.3,
in1 = 30, in2 = 60,
adj = "both",
alphaCImin = 0.1, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = 0.5, lambdamax = 1, steplambda = 0.05
)
expect_equal(res[1,]$Method, "multipl.")
expect_equal(res[1,]$n2, 206)
expect_equal(res[1,]$n3, 340)
expect_equal(res[1,]$n, 546)
expect_equal(res[1,]$u, 2116.67, tolerance = 0.005)
expect_equal(res[1,]$RRgo, 0.8)
expect_equal(res[1,]$Adj, 0.65)
expect_equal(res[2,]$Method, "add.")
expect_equal(res[2,]$n2, 190)
expect_equal(res[2,]$n3, 226)
expect_equal(res[2,]$n, 416)
expect_equal(res[2,]$u, 1996.10, tolerance = 0.005)
expect_equal(res[2,]$RRgo, 0.77)
expect_equal(res[2,]$Adj, 0.1)
})
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#' @editor Lukas D Sauer
#' @editDate 2022-12-07
test_that("02.01", {
# Adjustment method "additive" for time-to-event endpoints
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "additive",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = NULL, lambdamax = NULL, steplambda = NULL
)
expect_equal(res$n2, 122)
expect_equal(res$n3, 200)
expect_equal(res$n, 322)
expect_equal(res$u, 78, tolerance = 0.005)
expect_equal(res$HRgo, 0.78)
expect_equal(res$d2, 85)
expect_equal(res$d3, 140)
expect_equal(res$d, 225)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-07
test_that("02.02", {
# Adjustment method "multiplicative" for time-to-event endpoints
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "multiplicative",
alphaCImin = NULL, alphaCImax = NULL, stepalphaCI = NULL,
lambdamin = 0.5, lambdamax = 1, steplambda = 0.05
)
expect_equal(res$n2, 136)
expect_equal(res$n3, 244)
expect_equal(res$n, 380)
expect_equal(res$u, 99, tolerance = 0.005)
expect_equal(res$HRgo, 0.76)
expect_equal(res$pgo, 0.38)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-07
test_that("02.03", {
# Adjustment method "both" for time-to-event endpoints
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "both",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = 0.5, lambdamax = 1, steplambda = 0.05,
N = 350
)
expect_equal(res$Method, c("multipl.", "add."))
# Additive method
expect_equal(res[2,]$n2, 122)
expect_equal(res[2,]$n3, 200)
expect_equal(res[2,]$n, 322)
expect_equal(res[2,]$u, 78, tolerance = 0.005)
expect_equal(res[2,]$HRgo, 0.78)
expect_equal(res[2,]$d2, 85)
expect_equal(res[2,]$d3, 140)
expect_equal(res[2,]$d, 225)
# Multiplicative method
expect_equal(res[1,]$n2, 100)
expect_equal(res[1,]$n3, 240)
expect_equal(res[1,]$n, 340)
expect_equal(res[1,]$u, 98, tolerance = 0.01)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-07
test_that("02.04", {
# Adjustment method "all" for time-to-event endpoints
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "all",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = 0.5, lambdamax = 1, steplambda = 0.05,
N = 350
)
expect_equal(res$Method, c("multipl.", "add.", "multipl2.", "add2."))
# Additive
expect_equal(res[2,]$n2, 122)
expect_equal(res[2,]$n3, 200)
expect_equal(res[2,]$n, 322)
expect_equal(res[2,]$u, 78, tolerance = 0.005)
expect_equal(res[2,]$HRgo, 0.78)
expect_equal(res[2,]$d2, 85)
expect_equal(res[2,]$d3, 140)
expect_equal(res[2,]$d, 225)
# Multiplicative
expect_equal(res[1,]$n2, 100)
expect_equal(res[1,]$n3, 240)
expect_equal(res[1,]$n, 340)
expect_equal(res[1,]$u, 98, tolerance = 0.01)
# Multiplicative 2
expect_equal(res[3,]$n2, 108)
expect_equal(res[3,]$n3, 200)
expect_equal(res[3,]$n, 308)
expect_equal(res[3,]$u, 96.69, tolerance = 0.0001)
expect_equal(res[3,]$Adj, 0.75)
# Additive 2
expect_equal(res[4,]$n2, 136)
expect_equal(res[4,]$n3, 206)
expect_equal(res[4,]$n, 342)
expect_equal(res[4,]$u, 77.40, tolerance = 0.0001)
expect_equal(res[4,]$Adj, 0.475)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-13
test_that("02.05", {
# Without and with cost constraint
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = TRUE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "additive",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = NULL, lambdamax = NULL, steplambda = NULL
)
expect_equal(res$n2, 144)
expect_equal(res$n3, 478)
expect_equal(res$u, 865.02)
expect_equal(res$Adj, 0.5)
expect_equal(res$K2, 208)
expect_equal(res$K3, 608)
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = TRUE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "additive",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = NULL, lambdamax = NULL, steplambda = NULL,
K = 400
)
expect_equal(res$n2, 44)
expect_equal(res$n3, 172)
expect_equal(res$u, 474.18)
expect_equal(res$Adj, 0.475)
expect_equal(res$K2, 133)
expect_equal(res$K3, 263)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-14
test_that("02.06", {
# With and without probability constraint
# for time-to-event endpoints
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = TRUE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "additive",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = NULL, lambdamax = NULL, steplambda = NULL
)
expect_equal(res$n2, 144)
expect_equal(res$n3, 478)
expect_equal(res$u, 865.02)
expect_equal(res$Adj, 0.5)
expect_equal(res$sProg, 0.68)
expect_equal(res$sProg1, 0.07)
expect_equal(res$sProg2, 0.21)
expect_equal(res$sProg3, 0.39)
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = TRUE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "additive",
alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = NULL, lambdamax = NULL, steplambda = NULL,
S = 0.7
)
expect_equal(res$n2, 144)
expect_equal(res$n3, 552)
expect_equal(res$u, 857.77)
expect_equal(res$Adj, 0.5)
expect_equal(res$sProg, 0.7)
expect_equal(res$sProg1, 0.07)
expect_equal(res$sProg2, 0.21)
expect_equal(res$sProg3, 0.42)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-14
test_that("02.07", {
# No bias adjustment is equal to the basic setting
res <- optimal_bias(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
adj = "both",
alphaCImin = 0.5, alphaCImax = 0.5, stepalphaCI = 1,
lambdamin = 1, lambdamax = 1, steplambda = 1
)
expect_equal(res[1,]$n2, 122)
expect_equal(res[1,]$n3, 210)
expect_equal(res[1,]$n, 332)
expect_equal(res[1,]$u, 75.8, tolerance = 0.005)
expect_equal(res[2,]$n2, 122)
expect_equal(res[2,]$n3, 210)
expect_equal(res[2,]$n, 332)
expect_equal(res[2,]$u, 75.8, tolerance = 0.005)
res <- optimal_tte(alpha = 0.025,
beta = 0.1,
hr1 = 0.69, hr2 = 0.88,
xi2 = 0.7, xi3 = 0.7,
d2min = 20, d2max = 100, stepd2 = 5,
hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150, # fixed cost
c2 = 0.75, c3 = 1, # variable per patient cost
fixed = FALSE,
w = 0.3,
id1 = 210, id2 = 420,
)
expect_equal(res$n2, 122)
expect_equal(res$n3, 210)
expect_equal(res$n, 332)
expect_equal(res$u, 75.8, tolerance = 0.005)
})
# #' @editor Lukas D Sauer
# #' @editDate 2022-12-14
# test_that("02.08", {
# # Parallel computing works
# start_time_3 = Sys.time()
#
# res <- optimal_bias(alpha = 0.025,
# beta = 0.1,
# hr1 = 0.69, hr2 = 0.88,
# xi2 = 0.7, xi3 = 0.7,
# d2min = 20, d2max = 100, stepd2 = 5,
# hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
# steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
# b1 = 1000, b2 = 2000, b3 = 3000,
# num_cl = 12,
# c02 = 100, c03 = 150, # fixed cost
# c2 = 0.75, c3 = 1, # variable per patient cost
# fixed = FALSE,
# w = 0.3,
# id1 = 210, id2 = 420,
# adj = "additive",
# alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
# lambdamin = NULL, lambdamax = NULL, steplambda = NULL
# )
#
# end_time_3 = Sys.time()
# time_elapsed_num_cl_3 = end_time_3 - start_time_3
# start_time_1 = Sys.time()
#
# res <- optimal_bias(alpha = 0.025,
# beta = 0.1,
# hr1 = 0.69, hr2 = 0.88,
# xi2 = 0.7, xi3 = 0.7,
# d2min = 20, d2max = 100, stepd2 = 5,
# hrgomin = 0.7, hrgomax = 0.9, stephrgo = 0.02,
# steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
# b1 = 1000, b2 = 2000, b3 = 3000,
# num_cl = 6,
# c02 = 100, c03 = 150, # fixed cost
# c2 = 0.75, c3 = 1, # variable per patient cost
# fixed = FALSE,
# w = 0.3,
# id1 = 210, id2 = 420,
# adj = "additive",
# alphaCImin = 0.3, alphaCImax = 0.5, stepalphaCI = 0.025,
# lambdamin = NULL, lambdamax = NULL, steplambda = NULL
# )
#
# end_time_1 = Sys.time()
# time_elapsed_num_cl_1 = end_time_1 - start_time_1
# expect_true(time_elapsed_num_cl_1 > time_elapsed_num_cl_3)
# })
#' @editor Lukas D Sauer
#' @editDate 2022-12-14
test_that("02.09", {
# Bias adjustment for normally distributed endpoints
res <- optimal_bias_normal(
alpha = 0.05,
beta = 0.1,
Delta1 = 0.625, Delta2 = 0.325,
n2min = 20, n2max = 400, stepn2 = 4,
kappamin = 0.02, kappamax = 0.4, stepkappa = 0.02,
steps1 = 0, stepm1 = 0.5, stepl1 = 0.8,
b1 = 3000, b2 = 8000, b3 = 10000,
num_cl = 12,
c02 = 15, c03 = 20,
c2 = 0.675, c3 = 0.72,
fixed = FALSE,
w = 0.5,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75,
adj = "multiplicative",
lambdamin = 0.7, lambdamax = 0.9, steplambda = 0.01
)
expect_equal(res$n2, 192)
expect_equal(res$n3, 474)
expect_equal(res$n, 666)
expect_equal(res$Kappa, 0.12)
expect_equal(res$u, 2899.32, tolerance = 0.005)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-14
test_that("02.10", {
# Bias adjustment for normally distributed endpoints
# with fixed prior treatment effect and all adjustment
# methods
res <- optimal_bias_normal(
alpha = 0.05,
beta = 0.1,
Delta1 = 0.625, Delta2 = 0.325,
n2min = 20, n2max = 400, stepn2 = 4,
kappamin = 0.02, kappamax = 0.4, stepkappa = 0.02,
steps1 = 0, stepm1 = 0.5, stepl1 = 0.8,
b1 = 3000, b2 = 8000, b3 = 10000,
num_cl = 12,
c02 = 15, c03 = 20,
c2 = 0.675, c3 = 0.72,
fixed = TRUE,
w = 0.5,
in1 = 300, in2 = 600,
a = 0.25, b = 0.75,
adj = "all",
alphaCImin = 0.25, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = 0.7, lambdamax = 0.9, steplambda = 0.01
)
expect_equal(res$Method, c("multipl.", "add.", "multipl2.", "add2."))
# Multiplicative
expect_equal(res[1,]$n2, 88)
expect_equal(res[1,]$n3, 310)
expect_equal(res[1,]$n, 398)
expect_equal(res[1,]$u, 3861.76, tolerance = 0.0001)
expect_equal(res[1,]$Adj, 0.7, tolerance = 0.0001)
# Additive
expect_equal(res[2,]$n2, 96)
expect_equal(res[2,]$n3, 306)
expect_equal(res[2,]$n, 402)
expect_equal(res[2,]$u, 3631.51, tolerance = 0.0001)
expect_equal(res[2,]$Adj, 0.25, tolerance = 0.0001)
# Multiplicative 2
expect_equal(res[3,]$n2, 88)
expect_equal(res[3,]$n3, 306)
expect_equal(res[3,]$n, 394)
expect_equal(res[3,]$u, 3860.12, tolerance = 0.0001)
expect_equal(res[3,]$Adj, 0.7)
# Additive 2
expect_equal(res[4,]$n2, 96)
expect_equal(res[4,]$n3, 312)
expect_equal(res[4,]$n, 408)
expect_equal(res[4,]$u, 3631.28, tolerance = 0.0001)
expect_equal(res[4,]$Adj, 0.25)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-20
test_that("02.11", {
res <- optimal_bias_binary(
alpha = 0.025,
beta = 0.1,
p0 = 0.6, p11 = 0.3, p12= 0.5,
n2min = 10, n2max = 500, stepn2 = 2,
rrgomin = 0.7, rrgomax = 0.9, steprrgo = 0.01,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150,
c2 = 0.75, c3 = 1,
fixed = FALSE,
w = 0.3,
in1 = 30, in2 = 60,
adj = "additive",
alphaCImin = 0.1, alphaCImax = 0.5, stepalphaCI = 0.025,
)
expect_equal(res$n2, 166)
expect_equal(res$n3, 264)
expect_equal(res$n, 430)
expect_equal(res$RRgo, 0.82)
expect_equal(res$u, 605.91, tolerance = 0.005)
expect_equal(res$Adj, 0.275)
})
#' @editor Lukas D Sauer
#' @editDate 2022-12-20
test_that("02.12", {
res <- optimal_bias_binary(
alpha = 0.025,
beta = 0.1,
p0 = 0.6, p11 = 0.3, p12= 0.5,
n2min = 10, n2max = 500, stepn2 = 2,
rrgomin = 0.7, rrgomax = 0.9, steprrgo = 0.01,
steps1 = 1, stepm1 = 0.95, stepl1 = 0.85,
b1 = 1000, b2 = 2000, b3 = 3000,
num_cl = 12,
c02 = 100, c03 = 150,
c2 = 0.75, c3 = 1,
fixed = TRUE,
w = 0.3,
in1 = 30, in2 = 60,
adj = "both",
alphaCImin = 0.1, alphaCImax = 0.5, stepalphaCI = 0.025,
lambdamin = 0.5, lambdamax = 1, steplambda = 0.05
)
expect_equal(res[1,]$Method, "multipl.")
expect_equal(res[1,]$n2, 206)
expect_equal(res[1,]$n3, 340)
expect_equal(res[1,]$n, 546)
expect_equal(res[1,]$u, 2116.67, tolerance = 0.005)
expect_equal(res[1,]$RRgo, 0.8)
expect_equal(res[1,]$Adj, 0.65)
expect_equal(res[2,]$Method, "add.")
expect_equal(res[2,]$n2, 190)
expect_equal(res[2,]$n3, 226)
expect_equal(res[2,]$n, 416)
expect_equal(res[2,]$u, 1996.10, tolerance = 0.005)
expect_equal(res[2,]$RRgo, 0.77)
expect_equal(res[2,]$Adj, 0.1)
})
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