tests/testthat/_snaps/Rules-methods.md
In Roche/crmPack: Object-Oriented Implementation of CRM Designs
nextBest-NextBestNCRM returns expected values of the objects
Code
result$probs
Output
dose target overdose
[1,] 25 0.00 0.00
[2,] 50 0.00 0.00
[3,] 75 0.50 0.25
[4,] 100 0.25 0.50
[5,] 125 0.25 0.75
[6,] 150 0.00 1.00
[7,] 175 0.00 1.00
[8,] 200 0.00 1.00
[9,] 225 0.00 1.00
[10,] 250 0.00 1.00
[11,] 275 0.00 1.00
[12,] 300 0.00 1.00
nextBest-NextBestNCRM returns expected values of the objects (no doselimit)
Code
result$probs
Output
dose target overdose
[1,] 25 0.00 0.00
[2,] 50 0.00 0.00
[3,] 75 0.50 0.25
[4,] 100 0.25 0.50
[5,] 125 0.25 0.75
[6,] 150 0.00 1.00
[7,] 175 0.00 1.00
[8,] 200 0.00 1.00
[9,] 225 0.00 1.00
[10,] 250 0.00 1.00
[11,] 275 0.00 1.00
[12,] 300 0.00 1.00
nextBest-NextBestNCRM-DataParts returns expected values of the objects
Code
result$probs
Output
dose target overdose
[1,] 25 0.00 0.00
[2,] 50 0.00 0.00
[3,] 75 0.50 0.25
[4,] 100 0.25 0.50
[5,] 125 0.25 0.75
[6,] 150 0.00 1.00
[7,] 175 0.00 1.00
[8,] 200 0.00 1.00
[9,] 225 0.00 1.00
[10,] 250 0.00 1.00
[11,] 275 0.00 1.00
[12,] 300 0.00 1.00
nextBest-NextBestNCRM-DataParts returns expected values of the objects (no doselimit)
Code
result$probs
Output
dose target overdose
[1,] 25 0.00 0.00
[2,] 50 0.00 0.00
[3,] 75 0.50 0.25
[4,] 100 0.25 0.50
[5,] 125 0.25 0.75
[6,] 150 0.00 1.00
[7,] 175 0.00 1.00
[8,] 200 0.00 1.00
[9,] 225 0.00 1.00
[10,] 250 0.00 1.00
[11,] 275 0.00 1.00
[12,] 300 0.00 1.00
nextBest-NextBestNCRMLoss returns expected values of the objects
Code
result$probs
Output
dose underdosing target overdose mean std_dev posterior_loss
25 25 1.00 0.00 0.00 0.02399772 0.02468099 1.00
50 50 1.00 0.00 0.00 0.10786611 0.06384384 1.00
75 75 0.25 0.50 0.25 0.28084217 0.16237037 0.75
100 100 0.25 0.25 0.50 0.45071815 0.25848085 1.25
125 125 0.00 0.25 0.75 0.56989233 0.26271358 1.50
150 150 0.00 0.00 1.00 0.65868827 0.22931946 2.00
175 175 0.00 0.00 1.00 0.72753310 0.19117925 2.00
200 200 0.00 0.00 1.00 0.78065042 0.15910226 2.00
225 225 0.00 0.00 1.00 0.82128990 0.13465035 2.00
250 250 0.00 0.00 1.00 0.85237854 0.11638492 2.00
275 275 0.00 0.00 1.00 0.87633603 0.10248763 2.00
300 300 0.00 0.00 1.00 0.89501585 0.09155455 2.00
nextBest-NextBestNCRMLoss returns expected values of the objects (loss function of 4 elements)
Code
result$probs
Output
dose underdosing target excessive unacceptable mean std_dev
25 25 1.00 0.00 0.00 0.00 0.02399772 0.02468099
50 50 1.00 0.00 0.00 0.00 0.10786611 0.06384384
75 75 0.25 0.50 0.25 0.00 0.28084217 0.16237037
100 100 0.25 0.25 0.25 0.25 0.45071815 0.25848085
125 125 0.00 0.25 0.25 0.50 0.56989233 0.26271358
150 150 0.00 0.00 0.50 0.50 0.65868827 0.22931946
175 175 0.00 0.00 0.50 0.50 0.72753310 0.19117925
200 200 0.00 0.00 0.00 1.00 0.78065042 0.15910226
225 225 0.00 0.00 0.00 1.00 0.82128990 0.13465035
250 250 0.00 0.00 0.00 1.00 0.85237854 0.11638492
275 275 0.00 0.00 0.00 1.00 0.87633603 0.10248763
300 300 0.00 0.00 0.00 1.00 0.89501585 0.09155455
posterior_loss
25 1.00
50 1.00
75 0.50
100 1.00
125 1.25
150 1.50
175 1.50
200 2.00
225 2.00
250 2.00
275 2.00
300 2.00
nextBest-NextBestNCRMLoss returns expected values of the objects (no doselimit)
Code
result$probs
Output
dose underdosing target overdose mean std_dev posterior_loss
25 25 1.00 0.00 0.00 0.02399772 0.02468099 1.00
50 50 1.00 0.00 0.00 0.10786611 0.06384384 1.00
75 75 0.25 0.50 0.25 0.28084217 0.16237037 0.75
100 100 0.25 0.25 0.50 0.45071815 0.25848085 1.25
125 125 0.00 0.25 0.75 0.56989233 0.26271358 1.50
150 150 0.00 0.00 1.00 0.65868827 0.22931946 2.00
175 175 0.00 0.00 1.00 0.72753310 0.19117925 2.00
200 200 0.00 0.00 1.00 0.78065042 0.15910226 2.00
225 225 0.00 0.00 1.00 0.82128990 0.13465035 2.00
250 250 0.00 0.00 1.00 0.85237854 0.11638492 2.00
275 275 0.00 0.00 1.00 0.87633603 0.10248763 2.00
300 300 0.00 0.00 1.00 0.89501585 0.09155455 2.00
nextBest-NextBestDualEndpoint returns expected elements
Code
result$probs
Output
dose target overdose
[1,] 25 0.50 0.0
[2,] 50 0.00 0.0
[3,] 75 0.00 0.0
[4,] 100 0.00 0.5
[5,] 125 0.25 1.0
[6,] 150 0.00 1.0
[7,] 175 0.25 1.0
[8,] 200 0.00 1.0
[9,] 225 0.00 1.0
[10,] 250 0.00 1.0
[11,] 275 0.00 1.0
[12,] 300 0.00 1.0
nextBest-NextBestDualEndpoint returns expected elements (with Emax param)
Code
result$probs
Output
dose target overdose
[1,] 25 0 0.00
[2,] 50 0 0.00
[3,] 75 0 0.25
[4,] 100 0 0.75
[5,] 125 0 1.00
[6,] 150 0 1.00
[7,] 175 0 1.00
[8,] 200 0 1.00
[9,] 225 0 1.00
[10,] 250 0 1.00
[11,] 275 0 1.00
[12,] 300 0 1.00
nextBest-NextBestDualEndpoint returns expected elements (absolute target)
Code
result$probs
Output
dose target overdose
[1,] 25 0 0.00
[2,] 50 0 0.00
[3,] 75 0 0.00
[4,] 100 0 0.25
[5,] 125 0 1.00
[6,] 150 0 1.00
[7,] 175 0 1.00
[8,] 200 0 1.00
[9,] 225 0 1.00
[10,] 250 0 1.00
[11,] 275 0 1.00
[12,] 300 0 1.00
nextBest-NextBestDualEndpoint returns expected elements (absolute target, no doselimit)
Code
result$probs
Output
dose target overdose
[1,] 25 0 0.00
[2,] 50 0 0.00
[3,] 75 0 0.00
[4,] 100 0 0.25
[5,] 125 0 1.00
[6,] 150 0 1.00
[7,] 175 0 1.00
[8,] 200 0 1.00
[9,] 225 0 1.00
[10,] 250 0 1.00
[11,] 275 0 1.00
[12,] 300 0 1.00
nextBest-NextBestMinDist returns expected values and plot
Code
result$probs
Output
dose dlt_prob
[1,] 25 0.02399772
[2,] 50 0.10786611
[3,] 75 0.28084217
[4,] 100 0.45071815
[5,] 125 0.56989233
[6,] 150 0.65868827
[7,] 175 0.72753310
[8,] 200 0.78065042
[9,] 225 0.82128990
[10,] 250 0.85237854
[11,] 275 0.87633603
[12,] 300 0.89501585
nextBest-NextBestMinDist returns expected values and plot (with placebo)
Code
result$probs
Output
dose dlt_prob
[1,] 0.001 1.783989e-07
[2,] 25.000 4.216923e-01
[3,] 50.000 6.523699e-01
[4,] 75.000 7.600665e-01
[5,] 100.000 8.225213e-01
[6,] 125.000 8.624275e-01
[7,] 150.000 8.895790e-01
[8,] 175.000 9.089476e-01
[9,] 200.000 9.232921e-01
[10,] 225.000 9.342444e-01
[11,] 250.000 9.428199e-01
[12,] 275.000 9.496774e-01
[13,] 300.000 9.552602e-01
nextBest-NextBestMinDist returns expected values and plot (no doselimit)
Code
result$probs
Output
dose dlt_prob
[1,] 25 0.02399772
[2,] 50 0.10786611
[3,] 75 0.28084217
[4,] 100 0.45071815
[5,] 125 0.56989233
[6,] 150 0.65868827
[7,] 175 0.72753310
[8,] 200 0.78065042
[9,] 225 0.82128990
[10,] 250 0.85237854
[11,] 275 0.87633603
[12,] 300 0.89501585
nextBest-NextBestProbMTDLTE returns correct next dose and plot
Code
result$allocation
Output
dose allocation
25 25 0.00
50 50 0.25
75 75 0.25
100 100 0.00
125 125 0.50
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
nextBest-NextBestProbMTDLTE returns correct next dose and plot (with placebo)
Code
result$allocation
Output
dose allocation
25 25 0.75
50 50 0.00
75 75 0.25
100 100 0.00
125 125 0.00
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
nextBest-NextBestProbMTDLTE returns correct next dose and plot (no doselimit)
Code
result$allocation
Output
dose allocation
25 25 0.00
50 50 0.25
75 75 0.25
100 100 0.00
125 125 0.50
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
nextBest-NextBestProbMTDMinDist returns correct next dose and plot
Code
result$allocation
Output
dose allocation
25 25 0.00
50 50 0.00
75 75 0.25
100 100 0.25
125 125 0.50
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
nextBest-NextBestProbMTDMinDist returns correct next dose and plot (with placebo)
Code
result$allocation
Output
dose allocation
25 25 0.75
50 50 0.25
75 75 0.00
100 100 0.00
125 125 0.00
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
nextBest-NextBestProbMTDMinDist returns correct next dose and plot (no doselimit)
Code
result$allocation
Output
dose allocation
25 25 0.25
50 50 0.00
75 75 0.25
100 100 0.25
125 125 0.25
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
tidy-IncrementsRelative works correctly
structure(list(min = c(0, 20), max = c(20, Inf), increment = c(1,
0.33)), row.names = c(NA, -2L), class = c("tbl_IncrementsRelative",
"tbl_df", "tbl", "data.frame"))
structure(list(min = c(0, 20), max = c(20, Inf), increment = c(1,
0.33)), row.names = c(NA, -2L), class = c("tbl_IncrementsRelative",
"tbl_df", "tbl", "data.frame"))
tidy-CohortSizeDLT works correctly
structure(list(min = c(0, 1), max = c(1, Inf), cohort_size = c(1L,
3L)), row.names = c(NA, -2L), class = c("tbl_CohortSizeDLT",
"tbl_df", "tbl", "data.frame"))
tidy-CohortSizeMin works correctly
structure(list(structure(list(min = c(0, 10), max = c(10, Inf
), cohort_size = c(1L, 3L)), row.names = c(NA, -2L), class = c("tbl_CohortSizeRange",
"tbl_df", "tbl", "data.frame")), structure(list(min = c(0, 1),
max = c(1, Inf), cohort_size = c(1L, 3L)), row.names = c(NA,
-2L), class = c("tbl_CohortSizeDLT", "tbl_df", "tbl", "data.frame"
))), class = c("tbl_CohortSizeMin", "tbl_CohortSizeMin", "list"
))
tidy-CohortSizeMax works correctly
structure(list(structure(list(min = c(0, 10), max = c(10, Inf
), cohort_size = c(1L, 3L)), row.names = c(NA, -2L), class = c("tbl_CohortSizeRange",
"tbl_df", "tbl", "data.frame")), structure(list(min = c(0, 1),
max = c(1, Inf), cohort_size = c(1L, 3L)), row.names = c(NA,
-2L), class = c("tbl_CohortSizeDLT", "tbl_df", "tbl", "data.frame"
))), class = c("tbl_CohortSizeMax", "tbl_CohortSizeMax", "list"
))
tidy-CohortSizeRange works correctly
structure(list(min = c(0, 30), max = c(30, Inf), cohort_size = c(1L,
3L)), row.names = c(NA, -2L), class = c("tbl_CohortSizeRange",
"tbl_df", "tbl", "data.frame"))
tidy-CohortSizeParts works correctly
WAoAAAACAAQDAQACAwAAAAMTAAAAAgAAAA0AAAACAAAAAQAAAAIAAAANAAAAAgAAAAEAAAAD
AAAEAgAAAAEABAAJAAAABWNsYXNzAAAAEAAAAAQABAAJAAAAE3RibF9Db2hvcnRTaXplUGFy
dHMABAAJAAAABnRibF9kZgAEAAkAAAADdGJsAAQACQAAAApkYXRhLmZyYW1lAAAEAgAAAAEA
BAAJAAAACXJvdy5uYW1lcwAAAA0AAAACgAAAAP////4AAAQCAAAAAQAEAAkAAAAFbmFtZXMA
AAAQAAAAAgAEAAkAAAAEcGFydAAEAAkAAAALY29ob3J0X3NpemUAAAD+
tidy-IncrementsMin works correctly
structure(list(structure(list(min = c(0, 1, 3), max = c(1, 3,
Inf), increment = c(1, 0.33, 0.2)), row.names = c(NA, -3L), class = c("tbl_IncrementsRelativeDLT",
"tbl_df", "tbl", "data.frame")), structure(list(min = c(0, 20
), max = c(20, Inf), increment = c(1, 0.33)), row.names = c(NA,
-2L), class = c("tbl_IncrementsRelative", "tbl_df", "tbl", "data.frame"
))), class = c("tbl_IncrementsMin", "tbl_IncrementsMin", "list"
))
tidy-IncrementsRelativeParts works correctly
structure(list(dlt_start = structure(list(dlt_start = 0L), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -1L)), clean_start = structure(list(
clean_start = 1L), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -1L)), intervals = structure(list(intervals = c(0,
2)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-2L)), increments = structure(list(increments = c(2, 1)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -2L))), class = c("tbl_IncrementsRelativeParts",
"list"))
tidy-NextBestNCRMLoss works correctly
structure(list(Range = c("Underdose", "Target", "Overdose", "Unacceptable"
), Lower = c(0, 0.2, 0.35, 0.6), Upper = c(0.2, 0.35, 0.6, 1),
LossCoefficient = c(1, 0, 1, 2), MaxOverdoseProb = c(0.25,
0.25, 0.25, 0.25)), row.names = c(NA, -4L), class = c("tbl_NextBestNCRMLoss",
"tbl_df", "tbl", "data.frame"))
Roche/crmPack documentation built on June 30, 2024, 1:31 a.m.
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nextBest-NextBestNCRM returns expected values of the objects
Code
result$probs
Output
dose target overdose
[1,] 25 0.00 0.00
[2,] 50 0.00 0.00
[3,] 75 0.50 0.25
[4,] 100 0.25 0.50
[5,] 125 0.25 0.75
[6,] 150 0.00 1.00
[7,] 175 0.00 1.00
[8,] 200 0.00 1.00
[9,] 225 0.00 1.00
[10,] 250 0.00 1.00
[11,] 275 0.00 1.00
[12,] 300 0.00 1.00
nextBest-NextBestNCRM returns expected values of the objects (no doselimit)
Code
result$probs
Output
dose target overdose
[1,] 25 0.00 0.00
[2,] 50 0.00 0.00
[3,] 75 0.50 0.25
[4,] 100 0.25 0.50
[5,] 125 0.25 0.75
[6,] 150 0.00 1.00
[7,] 175 0.00 1.00
[8,] 200 0.00 1.00
[9,] 225 0.00 1.00
[10,] 250 0.00 1.00
[11,] 275 0.00 1.00
[12,] 300 0.00 1.00
nextBest-NextBestNCRM-DataParts returns expected values of the objects
Code
result$probs
Output
dose target overdose
[1,] 25 0.00 0.00
[2,] 50 0.00 0.00
[3,] 75 0.50 0.25
[4,] 100 0.25 0.50
[5,] 125 0.25 0.75
[6,] 150 0.00 1.00
[7,] 175 0.00 1.00
[8,] 200 0.00 1.00
[9,] 225 0.00 1.00
[10,] 250 0.00 1.00
[11,] 275 0.00 1.00
[12,] 300 0.00 1.00
nextBest-NextBestNCRM-DataParts returns expected values of the objects (no doselimit)
Code
result$probs
Output
dose target overdose
[1,] 25 0.00 0.00
[2,] 50 0.00 0.00
[3,] 75 0.50 0.25
[4,] 100 0.25 0.50
[5,] 125 0.25 0.75
[6,] 150 0.00 1.00
[7,] 175 0.00 1.00
[8,] 200 0.00 1.00
[9,] 225 0.00 1.00
[10,] 250 0.00 1.00
[11,] 275 0.00 1.00
[12,] 300 0.00 1.00
nextBest-NextBestNCRMLoss returns expected values of the objects
Code
result$probs
Output
dose underdosing target overdose mean std_dev posterior_loss
25 25 1.00 0.00 0.00 0.02399772 0.02468099 1.00
50 50 1.00 0.00 0.00 0.10786611 0.06384384 1.00
75 75 0.25 0.50 0.25 0.28084217 0.16237037 0.75
100 100 0.25 0.25 0.50 0.45071815 0.25848085 1.25
125 125 0.00 0.25 0.75 0.56989233 0.26271358 1.50
150 150 0.00 0.00 1.00 0.65868827 0.22931946 2.00
175 175 0.00 0.00 1.00 0.72753310 0.19117925 2.00
200 200 0.00 0.00 1.00 0.78065042 0.15910226 2.00
225 225 0.00 0.00 1.00 0.82128990 0.13465035 2.00
250 250 0.00 0.00 1.00 0.85237854 0.11638492 2.00
275 275 0.00 0.00 1.00 0.87633603 0.10248763 2.00
300 300 0.00 0.00 1.00 0.89501585 0.09155455 2.00
nextBest-NextBestNCRMLoss returns expected values of the objects (loss function of 4 elements)
Code
result$probs
Output
dose underdosing target excessive unacceptable mean std_dev
25 25 1.00 0.00 0.00 0.00 0.02399772 0.02468099
50 50 1.00 0.00 0.00 0.00 0.10786611 0.06384384
75 75 0.25 0.50 0.25 0.00 0.28084217 0.16237037
100 100 0.25 0.25 0.25 0.25 0.45071815 0.25848085
125 125 0.00 0.25 0.25 0.50 0.56989233 0.26271358
150 150 0.00 0.00 0.50 0.50 0.65868827 0.22931946
175 175 0.00 0.00 0.50 0.50 0.72753310 0.19117925
200 200 0.00 0.00 0.00 1.00 0.78065042 0.15910226
225 225 0.00 0.00 0.00 1.00 0.82128990 0.13465035
250 250 0.00 0.00 0.00 1.00 0.85237854 0.11638492
275 275 0.00 0.00 0.00 1.00 0.87633603 0.10248763
300 300 0.00 0.00 0.00 1.00 0.89501585 0.09155455
posterior_loss
25 1.00
50 1.00
75 0.50
100 1.00
125 1.25
150 1.50
175 1.50
200 2.00
225 2.00
250 2.00
275 2.00
300 2.00
nextBest-NextBestNCRMLoss returns expected values of the objects (no doselimit)
Code
result$probs
Output
dose underdosing target overdose mean std_dev posterior_loss
25 25 1.00 0.00 0.00 0.02399772 0.02468099 1.00
50 50 1.00 0.00 0.00 0.10786611 0.06384384 1.00
75 75 0.25 0.50 0.25 0.28084217 0.16237037 0.75
100 100 0.25 0.25 0.50 0.45071815 0.25848085 1.25
125 125 0.00 0.25 0.75 0.56989233 0.26271358 1.50
150 150 0.00 0.00 1.00 0.65868827 0.22931946 2.00
175 175 0.00 0.00 1.00 0.72753310 0.19117925 2.00
200 200 0.00 0.00 1.00 0.78065042 0.15910226 2.00
225 225 0.00 0.00 1.00 0.82128990 0.13465035 2.00
250 250 0.00 0.00 1.00 0.85237854 0.11638492 2.00
275 275 0.00 0.00 1.00 0.87633603 0.10248763 2.00
300 300 0.00 0.00 1.00 0.89501585 0.09155455 2.00
nextBest-NextBestDualEndpoint returns expected elements
Code
result$probs
Output
dose target overdose
[1,] 25 0.50 0.0
[2,] 50 0.00 0.0
[3,] 75 0.00 0.0
[4,] 100 0.00 0.5
[5,] 125 0.25 1.0
[6,] 150 0.00 1.0
[7,] 175 0.25 1.0
[8,] 200 0.00 1.0
[9,] 225 0.00 1.0
[10,] 250 0.00 1.0
[11,] 275 0.00 1.0
[12,] 300 0.00 1.0
nextBest-NextBestDualEndpoint returns expected elements (with Emax param)
Code
result$probs
Output
dose target overdose
[1,] 25 0 0.00
[2,] 50 0 0.00
[3,] 75 0 0.25
[4,] 100 0 0.75
[5,] 125 0 1.00
[6,] 150 0 1.00
[7,] 175 0 1.00
[8,] 200 0 1.00
[9,] 225 0 1.00
[10,] 250 0 1.00
[11,] 275 0 1.00
[12,] 300 0 1.00
nextBest-NextBestDualEndpoint returns expected elements (absolute target)
Code
result$probs
Output
dose target overdose
[1,] 25 0 0.00
[2,] 50 0 0.00
[3,] 75 0 0.00
[4,] 100 0 0.25
[5,] 125 0 1.00
[6,] 150 0 1.00
[7,] 175 0 1.00
[8,] 200 0 1.00
[9,] 225 0 1.00
[10,] 250 0 1.00
[11,] 275 0 1.00
[12,] 300 0 1.00
nextBest-NextBestDualEndpoint returns expected elements (absolute target, no doselimit)
Code
result$probs
Output
dose target overdose
[1,] 25 0 0.00
[2,] 50 0 0.00
[3,] 75 0 0.00
[4,] 100 0 0.25
[5,] 125 0 1.00
[6,] 150 0 1.00
[7,] 175 0 1.00
[8,] 200 0 1.00
[9,] 225 0 1.00
[10,] 250 0 1.00
[11,] 275 0 1.00
[12,] 300 0 1.00
nextBest-NextBestMinDist returns expected values and plot
Code
result$probs
Output
dose dlt_prob
[1,] 25 0.02399772
[2,] 50 0.10786611
[3,] 75 0.28084217
[4,] 100 0.45071815
[5,] 125 0.56989233
[6,] 150 0.65868827
[7,] 175 0.72753310
[8,] 200 0.78065042
[9,] 225 0.82128990
[10,] 250 0.85237854
[11,] 275 0.87633603
[12,] 300 0.89501585
nextBest-NextBestMinDist returns expected values and plot (with placebo)
Code
result$probs
Output
dose dlt_prob
[1,] 0.001 1.783989e-07
[2,] 25.000 4.216923e-01
[3,] 50.000 6.523699e-01
[4,] 75.000 7.600665e-01
[5,] 100.000 8.225213e-01
[6,] 125.000 8.624275e-01
[7,] 150.000 8.895790e-01
[8,] 175.000 9.089476e-01
[9,] 200.000 9.232921e-01
[10,] 225.000 9.342444e-01
[11,] 250.000 9.428199e-01
[12,] 275.000 9.496774e-01
[13,] 300.000 9.552602e-01
nextBest-NextBestMinDist returns expected values and plot (no doselimit)
Code
result$probs
Output
dose dlt_prob
[1,] 25 0.02399772
[2,] 50 0.10786611
[3,] 75 0.28084217
[4,] 100 0.45071815
[5,] 125 0.56989233
[6,] 150 0.65868827
[7,] 175 0.72753310
[8,] 200 0.78065042
[9,] 225 0.82128990
[10,] 250 0.85237854
[11,] 275 0.87633603
[12,] 300 0.89501585
nextBest-NextBestProbMTDLTE returns correct next dose and plot
Code
result$allocation
Output
dose allocation
25 25 0.00
50 50 0.25
75 75 0.25
100 100 0.00
125 125 0.50
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
nextBest-NextBestProbMTDLTE returns correct next dose and plot (with placebo)
Code
result$allocation
Output
dose allocation
25 25 0.75
50 50 0.00
75 75 0.25
100 100 0.00
125 125 0.00
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
nextBest-NextBestProbMTDLTE returns correct next dose and plot (no doselimit)
Code
result$allocation
Output
dose allocation
25 25 0.00
50 50 0.25
75 75 0.25
100 100 0.00
125 125 0.50
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
nextBest-NextBestProbMTDMinDist returns correct next dose and plot
Code
result$allocation
Output
dose allocation
25 25 0.00
50 50 0.00
75 75 0.25
100 100 0.25
125 125 0.50
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
nextBest-NextBestProbMTDMinDist returns correct next dose and plot (with placebo)
Code
result$allocation
Output
dose allocation
25 25 0.75
50 50 0.25
75 75 0.00
100 100 0.00
125 125 0.00
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
nextBest-NextBestProbMTDMinDist returns correct next dose and plot (no doselimit)
Code
result$allocation
Output
dose allocation
25 25 0.25
50 50 0.00
75 75 0.25
100 100 0.25
125 125 0.25
150 150 0.00
175 175 0.00
200 200 0.00
225 225 0.00
250 250 0.00
275 275 0.00
300 300 0.00
tidy-IncrementsRelative works correctly
structure(list(min = c(0, 20), max = c(20, Inf), increment = c(1,
0.33)), row.names = c(NA, -2L), class = c("tbl_IncrementsRelative",
"tbl_df", "tbl", "data.frame"))
structure(list(min = c(0, 20), max = c(20, Inf), increment = c(1,
0.33)), row.names = c(NA, -2L), class = c("tbl_IncrementsRelative",
"tbl_df", "tbl", "data.frame"))
tidy-CohortSizeDLT works correctly
structure(list(min = c(0, 1), max = c(1, Inf), cohort_size = c(1L,
3L)), row.names = c(NA, -2L), class = c("tbl_CohortSizeDLT",
"tbl_df", "tbl", "data.frame"))
tidy-CohortSizeMin works correctly
structure(list(structure(list(min = c(0, 10), max = c(10, Inf
), cohort_size = c(1L, 3L)), row.names = c(NA, -2L), class = c("tbl_CohortSizeRange",
"tbl_df", "tbl", "data.frame")), structure(list(min = c(0, 1),
max = c(1, Inf), cohort_size = c(1L, 3L)), row.names = c(NA,
-2L), class = c("tbl_CohortSizeDLT", "tbl_df", "tbl", "data.frame"
))), class = c("tbl_CohortSizeMin", "tbl_CohortSizeMin", "list"
))
tidy-CohortSizeMax works correctly
structure(list(structure(list(min = c(0, 10), max = c(10, Inf
), cohort_size = c(1L, 3L)), row.names = c(NA, -2L), class = c("tbl_CohortSizeRange",
"tbl_df", "tbl", "data.frame")), structure(list(min = c(0, 1),
max = c(1, Inf), cohort_size = c(1L, 3L)), row.names = c(NA,
-2L), class = c("tbl_CohortSizeDLT", "tbl_df", "tbl", "data.frame"
))), class = c("tbl_CohortSizeMax", "tbl_CohortSizeMax", "list"
))
tidy-CohortSizeRange works correctly
structure(list(min = c(0, 30), max = c(30, Inf), cohort_size = c(1L,
3L)), row.names = c(NA, -2L), class = c("tbl_CohortSizeRange",
"tbl_df", "tbl", "data.frame"))
tidy-CohortSizeParts works correctly
WAoAAAACAAQDAQACAwAAAAMTAAAAAgAAAA0AAAACAAAAAQAAAAIAAAANAAAAAgAAAAEAAAAD
AAAEAgAAAAEABAAJAAAABWNsYXNzAAAAEAAAAAQABAAJAAAAE3RibF9Db2hvcnRTaXplUGFy
dHMABAAJAAAABnRibF9kZgAEAAkAAAADdGJsAAQACQAAAApkYXRhLmZyYW1lAAAEAgAAAAEA
BAAJAAAACXJvdy5uYW1lcwAAAA0AAAACgAAAAP////4AAAQCAAAAAQAEAAkAAAAFbmFtZXMA
AAAQAAAAAgAEAAkAAAAEcGFydAAEAAkAAAALY29ob3J0X3NpemUAAAD+
tidy-IncrementsMin works correctly
structure(list(structure(list(min = c(0, 1, 3), max = c(1, 3,
Inf), increment = c(1, 0.33, 0.2)), row.names = c(NA, -3L), class = c("tbl_IncrementsRelativeDLT",
"tbl_df", "tbl", "data.frame")), structure(list(min = c(0, 20
), max = c(20, Inf), increment = c(1, 0.33)), row.names = c(NA,
-2L), class = c("tbl_IncrementsRelative", "tbl_df", "tbl", "data.frame"
))), class = c("tbl_IncrementsMin", "tbl_IncrementsMin", "list"
))
tidy-IncrementsRelativeParts works correctly
structure(list(dlt_start = structure(list(dlt_start = 0L), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -1L)), clean_start = structure(list(
clean_start = 1L), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -1L)), intervals = structure(list(intervals = c(0,
2)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-2L)), increments = structure(list(increments = c(2, 1)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -2L))), class = c("tbl_IncrementsRelativeParts",
"list"))
tidy-NextBestNCRMLoss works correctly
structure(list(Range = c("Underdose", "Target", "Overdose", "Unacceptable"
), Lower = c(0, 0.2, 0.35, 0.6), Upper = c(0.2, 0.35, 0.6, 1),
LossCoefficient = c(1, 0, 1, 2), MaxOverdoseProb = c(0.25,
0.25, 0.25, 0.25)), row.names = c(NA, -4L), class = c("tbl_NextBestNCRMLoss",
"tbl_df", "tbl", "data.frame"))
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