tests/testthat/_snaps/Design-methods.md
In Roche/crmPack: Object-Oriented Implementation of CRM Designs
simulate produces consistent results with placebo data
Code
result
Output
An object of class "Simulations"
Slot "fit":
[[1]]
middle lower upper
1 0.002380255 0.000638856 0.007082031
2 0.014222189 0.008233734 0.030391015
3 0.036277884 0.027439857 0.060140558
4 0.056826691 0.047465309 0.082102421
5 0.104528948 0.097325308 0.123978777
6 0.147987480 0.144820674 0.156537858
7 0.187713911 0.183810054 0.189159784
8 0.224084458 0.207488656 0.230231052
9 0.316307005 0.264631442 0.335446102
10 0.366443100 0.295038058 0.392889412
11 0.479544075 0.364837154 0.522028120
12 0.533826996 0.400048833 0.583374464
Slot "stop_report":
<NA> <NA> ≥ 3 cohorts dosed P(0.2 ≤ prob(DLE | NBD) ≤ 0.35) ≥ 0.5
[1,] TRUE FALSE TRUE FALSE
≥ 20 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[1] "Number of cohorts is 5 and thus reached the prespecified minimum number 3"
[[1]][[1]][[2]]
[1] "Probability for target toxicity is 0 % for dose 1 and thus below the required 50 %"
[[1]][[2]]
[1] "Number of patients is 20 and thus reached the prespecified minimum number 20"
Slot "additional_stats":
[[1]]
list()
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 0.1 3.0 3.0 3.0 0.1 1.0 1.0 1.0 0.1 1.0 1.0 1.0 0.1 1.0 1.0 1.0 0.1 1.0 1.0
[20] 1.0
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Slot "doseGrid":
[1] 0.1 1.0 3.0 5.0 10.0 15.0 20.0 25.0 40.0 50.0 80.0 100.0
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 1 3 3 3 1 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2
Slot "placebo":
[1] TRUE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Slot "cohort":
[1] 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5
Slot "nObs":
[1] 20
Slot "doses":
[1] 1
Slot "seed":
[1] 819
simulate produces consistent results with sentinel patients
Code
result
Output
An object of class "Simulations"
Slot "fit":
[[1]]
middle lower upper
1 0.002395946 0.0006466696 0.007118992
2 0.014321117 0.0083103333 0.030550234
3 0.036517541 0.0276538514 0.060449502
4 0.057182146 0.0477992422 0.082515987
5 0.105108928 0.0978987592 0.124576383
6 0.148728222 0.1455668979 0.157263798
7 0.188570939 0.1846333002 0.190029324
8 0.225025224 0.2083888362 0.231186850
9 0.317375596 0.2656888966 0.336518818
10 0.367536873 0.2961634016 0.393971492
11 0.480600330 0.3660795717 0.523015426
12 0.534826616 0.4013310812 0.584269407
Slot "stop_report":
<NA> <NA> ≥ 3 cohorts dosed P(0.2 ≤ prob(DLE | NBD) ≤ 0.35) ≥ 0.5
[1,] TRUE FALSE TRUE FALSE
≥ 20 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[1] "Number of cohorts is 7 and thus reached the prespecified minimum number 3"
[[1]][[1]][[2]]
[1] "Probability for target toxicity is 0 % for dose 1 and thus below the required 50 %"
[[1]][[2]]
[1] "Number of patients is 21 and thus reached the prespecified minimum number 20"
Slot "additional_stats":
[[1]]
list()
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Slot "doseGrid":
[1] 0.1 1.0 3.0 5.0 10.0 15.0 20.0 25.0 40.0 50.0 80.0 100.0
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7
Slot "nObs":
[1] 21
Slot "doses":
[1] 1
Slot "seed":
[1] 819
simulate-RuleDesign produces consistent results
Code
result
Output
An object of class "GeneralSimulations"
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 1 1 1 3 3 3 5 5 5 10 10 10 15 15 15 20 20 20 25 25 25 25 25 25 40
[26] 40 40 50 50 50 80 80 80 50 50 50
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
Slot "doseGrid":
[1] 1 3 5 10 15 20 25 40 50 80 100
Slot "nGrid":
[1] 11
Slot "xLevel":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 7 7 7 8
[26] 8 8 9 9 9 10 10 10 9 9 9
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 8 8 8 9
[26] 9 9 10 10 10 11 11 11 12 12 12
Slot "nObs":
[1] 36
Slot "doses":
[1] 50
Slot "seed":
[1] 819
Code
result
Output
dose DLTs nextDose stop increment
1 1 0 3 FALSE 200
2 1 1 1 FALSE 0
3 1 2 NA TRUE NA
4 1 3 NA TRUE NA
5 3 0 5 FALSE 67
6 3 1 3 FALSE 0
7 3 2 1 FALSE -67
8 3 3 1 FALSE -67
9 5 0 10 FALSE 100
10 5 1 5 FALSE 0
11 5 2 3 FALSE -40
12 5 3 3 FALSE -40
13 10 0 15 FALSE 50
14 10 1 10 FALSE 0
15 10 2 5 FALSE -50
16 10 3 5 FALSE -50
17 15 0 20 FALSE 33
18 15 1 15 FALSE 0
19 15 2 10 FALSE -33
20 15 3 10 FALSE -33
21 20 0 25 FALSE 25
22 20 1 20 FALSE 0
23 20 2 15 FALSE -25
24 20 3 15 FALSE -25
25 25 0 40 FALSE 60
26 25 1 25 FALSE 0
27 25 2 20 FALSE -20
28 25 3 20 FALSE -20
29 40 0 50 FALSE 25
30 40 1 40 FALSE 0
31 40 2 25 FALSE -38
32 40 3 25 FALSE -38
33 50 0 80 FALSE 60
34 50 1 50 FALSE 0
35 50 2 40 FALSE -20
36 50 3 40 FALSE -20
37 80 0 100 FALSE 25
38 80 1 80 FALSE 0
39 80 2 50 FALSE -38
40 80 3 50 FALSE -38
simulate-DualDesign produces consistent results
Code
result
Output
An object of class "DualSimulations"
Slot "rho_est":
[1] 0.07991541
Slot "sigma2w_est":
[1] 0.03177778
Slot "fit_biomarker":
[[1]]
middleBiomarker lowerBiomarker upperBiomarker
1 0.2434966 0.13294431 0.3701356
2 0.2325369 0.07576239 0.3763400
3 0.2284404 -0.07077652 0.5703187
4 0.2522588 -0.36780215 0.7826113
5 0.2364434 -0.49574988 0.9677055
6 0.2198298 -0.58828157 0.9583787
7 0.2185295 -0.71740079 1.1194549
8 0.2034642 -0.98358012 1.3289780
9 0.2058875 -1.12465077 1.4500549
10 0.1976203 -1.48546712 1.7065673
11 0.1905852 -1.80896626 2.0767703
Slot "fit":
[[1]]
middle lower upper
1 0.01093716 0.0009962211 0.04095732
2 0.22473302 0.0526930358 0.68081948
3 0.43571994 0.0935359919 0.97409485
4 0.67981427 0.1808020000 0.99996023
5 0.77864284 0.2500773718 0.99999984
6 0.82773116 0.3066616729 1.00000000
7 0.85641581 0.3540839546 1.00000000
8 0.89924516 0.4608241419 1.00000000
9 0.91428153 0.5130270841 1.00000000
10 0.93939515 0.6211946840 1.00000000
11 0.94903699 0.6699001621 1.00000000
Slot "stop_report":
<NA> P(0.9 ≤ Biomarker ≤ 1) ≥ 0.5 (relative) ≥ 10 patients dosed
[1,] TRUE FALSE TRUE
Slot "stop_reasons":
[[1]]
[[1]][[1]]
[1] "Probability for target biomarker is 21 % for dose 1 and thus below the required 50 %"
[[1]][[2]]
[1] "Number of patients is 12 and thus reached the prespecified minimum number 10"
Slot "additional_stats":
[[1]]
list()
Slot "data":
[[1]]
An object of class "DataDual"
Slot "w":
[1] 0.2557299 0.1150998 0.3181927 0.2531184 0.1632822 0.3616207 0.2672235
[8] 0.1000139 0.1305151 0.2393188 0.3006751 0.2951640
Slot "x":
[1] 3 3 3 1 1 1 1 1 1 1 1 1
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0
Slot "doseGrid":
[1] 1 3 5 10 15 20 25 40 50 80 100
Slot "nGrid":
[1] 11
Slot "xLevel":
[1] 2 2 2 1 1 1 1 1 1 1 1 1
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4
Slot "nObs":
[1] 12
Slot "doses":
[1] 1
Slot "seed":
[1] 3
simulate-TDSamplesDesign produces consistent results
Code
result
Output
An object of class "PseudoSimulations"
Slot "fit":
[[1]]
middle lower upper
1 0.08583626 0.02314306 0.1615121
2 0.13700263 0.05461354 0.2354364
3 0.17983349 0.08156022 0.2883433
4 0.21717502 0.10142184 0.3323753
5 0.25027602 0.12585805 0.3964506
6 0.27989888 0.14829847 0.4402591
7 0.30659033 0.15645905 0.4676890
8 0.33077494 0.16457914 0.5086352
9 0.35279588 0.16964809 0.5446742
10 0.37293640 0.17428698 0.5765315
11 0.39143316 0.17856908 0.6048191
12 0.40848551 0.18254982 0.6300498
Slot "final_td_target_during_trial_estimates":
[1] 169.4013
Slot "final_td_target_end_of_trial_estimates":
[1] 131.4969
Slot "final_td_target_during_trial_at_dose_grid":
[1] 150
Slot "final_td_target_end_of_trial_at_dose_grid":
[1] 125
Slot "final_tdeot_cis":
[[1]]
[[1]]$lower
[1] 81.58873
[[1]]$upper
[1] 1429.96
Slot "final_tdeot_ratios":
[1] 17.52644
Slot "final_cis":
[[1]]
[[1]]$lower
[1] 81.58873
[[1]]$upper
[1] 1429.96
Slot "final_ratios":
[1] 17.52644
Slot "stop_report":
≥ 36 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[1] "Number of patients is 36 and thus reached the prespecified minimum number 36"
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 25 25 25 50 50 50 75 75 75 100 100 100 125 125 125 300 300 300 75
[20] 75 75 125 125 125 150 150 150 150 150 150 225 225 225 150 150 150
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0
Slot "doseGrid":
[1] 25 50 75 100 125 150 175 200 225 250 275 300
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 12 12 12 3 3 3 5 5 5 6
[26] 6 6 6 6 6 9 9 9 6 6 6
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 8 8 8 9
[26] 9 9 10 10 10 11 11 11 12 12 12
Slot "nObs":
[1] 36
Slot "doses":
[1] 125
Slot "seed":
[1] 819
simulate-TDDesign produces consistent results
Code
result
Output
An object of class "PseudoSimulations"
Slot "fit":
[[1]]
[[1]]$phi1
[1] -9.791609
[[1]]$phi2
[1] 1.826935
[[1]]$probDLE
[1] 0.01962880 0.06632269 0.12967442 0.20128792 0.27476182 0.34581054
[7] 0.41195661 0.47204678 0.52579017 0.57340058 0.61535170 0.65222382
Slot "final_td_target_during_trial_estimates":
[1] 151.5239
Slot "final_td_target_end_of_trial_estimates":
[1] 133.7273
Slot "final_td_target_during_trial_at_dose_grid":
[1] 150
Slot "final_td_target_end_of_trial_at_dose_grid":
[1] 125
Slot "final_tdeot_cis":
[[1]]
[[1]]$lower
[1] 90.1733
[[1]]$upper
[1] 198.318
Slot "final_tdeot_ratios":
[1] 2.199298
Slot "final_cis":
[[1]]
[[1]]$lower
[1] 90.1733
[[1]]$upper
[1] 198.318
Slot "final_ratios":
[1] 2.199298
Slot "stop_report":
≥ 36 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[1] "Number of patients is 36 and thus reached the prespecified minimum number 36"
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 50 50 50 100 100 100 200 200 200 75 75 75 100 100 100 125 125 125 150
[20] 150 150 150 150 150 125 125 125 150 150 150 150 150 150 125 125 125
Slot "y":
[1] 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 1 0 0 0
Slot "doseGrid":
[1] 25 50 75 100 125 150 175 200 225 250 275 300
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 2 2 2 4 4 4 8 8 8 3 3 3 4 4 4 5 5 5 6 6 6 6 6 6 5 5 5 6 6 6 6 6 6 5 5 5
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 8 8 8 9
[26] 9 9 10 10 10 11 11 11 12 12 12
Slot "nObs":
[1] 36
Slot "doses":
[1] 125
Slot "seed":
[1] 819
simulate-DualResponsesDesign produces consistent results
Code
result
Output
An object of class "PseudoDualSimulations"
Slot "fitEff":
[[1]]
[[1]]$theta1
[1] -4.20662
[[1]]$theta2
[1] 3.302767
[[1]]$ExpEff
[1] -0.3455793 0.2985344 0.6242505 0.8372982 0.9935774 1.1159959
[7] 1.2160729 1.3003781 1.3729953 1.4366277 1.4931529 1.5439249
Slot "FinalGstarEstimates":
[1] 146.2479
Slot "FinalGstarAtDoseGrid":
[1] 125
Slot "FinalGstarCIs":
[[1]]
[[1]]$lower
[1] 75.03531
[[1]]$upper
[1] 285.0453
Slot "FinalGstarRatios":
[1] 3.798815
Slot "FinalOptimalDose":
[1] 137.5996
Slot "FinalOptimalDoseAtDoseGrid":
[1] 125
Slot "sigma2est":
[1] 0.1616952
Slot "fit":
[[1]]
[[1]]$phi1
[1] -9.998377
[[1]]$phi2
[1] 1.858333
[[1]]$probDLE
[1] 0.01769477 0.06131043 0.12184797 0.19147776 0.26390666 0.33471252
[7] 0.40119608 0.46198895 0.51662937 0.56521068 0.60812916 0.64591963
Slot "final_td_target_during_trial_estimates":
[1] 155.5827
Slot "final_td_target_end_of_trial_estimates":
[1] 137.5996
Slot "final_td_target_during_trial_at_dose_grid":
[1] 150
Slot "final_td_target_end_of_trial_at_dose_grid":
[1] 125
Slot "final_tdeot_cis":
[[1]]
[[1]]$lower
[1] 92.64601
[[1]]$upper
[1] 204.3655
Slot "final_tdeot_ratios":
[1] 2.205875
Slot "final_cis":
[[1]]
[[1]]$lower
[1] 92.64601
[[1]]$upper
[1] 204.3655
Slot "final_ratios":
[1] 2.205875
Slot "stop_report":
≥ 36 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[1] "Number of patients is 36 and thus reached the prespecified minimum number 36"
Slot "data":
[[1]]
An object of class "DataDual"
Slot "w":
[1] -0.6588212 -0.6574900 -0.6767438 0.4201327 0.1657673 0.6398290
[7] 1.0794245 1.0493776 0.9825006 1.6985308 1.7758098 1.5194703
[13] 1.1747540 0.7687320 0.5822763 0.8930588 1.0287153 0.9338591
[19] 1.1158580 1.0035394 1.0517295 1.5133967 1.2179947 1.0877295
[25] 1.6171994 1.2565239 1.0710927 0.9289380 0.8863580 0.8356701
[31] 0.7950787 0.6793477 0.7302074 0.7626097 0.8732485 0.8272182
Slot "x":
[1] 25 25 25 75 75 75 125 125 125 250 250 250 100 100 100 125 125 125 150
[20] 150 150 150 150 150 175 175 175 125 125 125 125 125 125 125 125 125
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0 0
Slot "doseGrid":
[1] 25 50 75 100 125 150 175 200 225 250 275 300
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 1 1 1 3 3 3 5 5 5 10 10 10 4 4 4 5 5 5 6 6 6 6 6 6 7
[26] 7 7 5 5 5 5 5 5 5 5 5
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 8 8 8 9
[26] 9 9 10 10 10 11 11 11 12 12 12
Slot "nObs":
[1] 36
Slot "doses":
[1] 125
Slot "seed":
[1] 819
simulate-DualResponsesSamplesDesign produces consistent results
Code
result
Output
An object of class "PseudoDualSimulations"
Slot "fitEff":
[[1]]
middle lower upper
1 -0.3079474 -0.8521410 0.08196096
2 0.3736494 0.1411489 0.55468787
3 0.7183200 0.5077815 0.88538501
4 0.9437657 0.7214829 1.15179652
5 1.1091393 0.8738198 1.41328216
6 1.2386817 0.9809589 1.61908158
7 1.3445825 1.0555782 1.78343868
8 1.4337937 1.1087482 1.89651747
9 1.5106368 1.1545466 1.99391914
10 1.5779722 1.1946786 2.07926954
11 1.6377868 1.2303281 2.15508685
12 1.6915134 1.2623492 2.22318756
Slot "FinalGstarEstimates":
[1] 300
Slot "FinalGstarAtDoseGrid":
[1] 225
Slot "FinalGstarCIs":
[[1]]
[[1]]$lower
[1] 300
[[1]]$upper
[1] 300
Slot "FinalGstarRatios":
[1] 1
Slot "FinalOptimalDose":
[1] 62.78087
Slot "FinalOptimalDoseAtDoseGrid":
[1] 50
Slot "sigma2est":
[1] 0.2648646
Slot "fit":
[[1]]
middle lower upper
1 0.1627044 0.07680748 0.2713562
2 0.2061350 0.09015426 0.3239337
3 0.2352925 0.09889961 0.3570465
4 0.2575780 0.10555513 0.3819918
5 0.2757086 0.11098665 0.4045258
6 0.2910230 0.11560460 0.4232458
7 0.3042901 0.11963849 0.4392495
8 0.3159958 0.12323056 0.4532166
9 0.3264687 0.12647556 0.4655986
10 0.3359419 0.12943997 0.4767113
11 0.3445874 0.13217237 0.4867840
12 0.3525358 0.13470940 0.4960901
Slot "final_td_target_during_trial_estimates":
[1] 95.95589
Slot "final_td_target_end_of_trial_estimates":
[1] 62.78087
Slot "final_td_target_during_trial_at_dose_grid":
[1] 75
Slot "final_td_target_end_of_trial_at_dose_grid":
[1] 50
Slot "final_tdeot_cis":
[[1]]
[[1]]$lower
[1] 62.78087
[[1]]$upper
[1] 62.78087
Slot "final_tdeot_ratios":
[1] 1
Slot "final_cis":
[[1]]
[[1]]$lower
[1] 62.78087
[[1]]$upper
[1] 62.78087
Slot "final_ratios":
[1] 1
Slot "stop_report":
≥ 10 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[1] "Number of patients is 12 and thus reached the prespecified minimum number 10"
Slot "data":
[[1]]
An object of class "DataDual"
Slot "w":
[1] -0.6588212 -0.6574900 -0.6767438 0.5106132 0.5020599 0.5558647
[7] 1.0105606 0.8984411 1.2273833 0.6454706 0.5818376 0.4685371
Slot "x":
[1] 25 25 25 75 75 75 125 125 125 75 75 75
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0
Slot "doseGrid":
[1] 25 50 75 100 125 150 175 200 225 250 275 300
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 1 1 1 3 3 3 5 5 5 3 3 3
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4
Slot "nObs":
[1] 12
Slot "doses":
[1] 50
Slot "seed":
[1] 819
Test if simulate generate the expected output.
Code
sim
Output
An object of class "Simulations"
Slot "fit":
[[1]]
middle lower upper
1 0.3185617 0.1610002 0.4380715
2 0.3115668 0.2305102 0.4260590
3 0.3123386 0.2023495 0.4190715
4 0.3148504 0.1839781 0.4141332
5 0.3177190 0.1706377 0.4250526
6 0.3205237 0.1603176 0.4615404
7 0.3231301 0.1519912 0.4927203
8 0.3255041 0.1450683 0.5197231
9 0.3276489 0.1391811 0.5433694
10 0.3295813 0.1340857 0.5642717
11 0.3313223 0.1296131 0.5828979
12 0.3328926 0.1256414 0.5996128
Slot "stop_report":
≥ 5 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[1] "Number of patients is 15 and thus reached the prespecified minimum number 5"
Slot "additional_stats":
[[1]]
list()
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 25 25 25 25 50 50 50 50 100 100 100 100 25 25 25
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1
Slot "doseGrid":
[1] 25 50 75 100 125 150 175 200 225 250 275 300
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 1 1 1 1 2 2 2 2 4 4 4 4 1 1 1
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Slot "cohort":
[1] 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4
Slot "nObs":
[1] 15
Slot "doses":
[1] NA
Slot "seed":
[1] 819
NextBestInfTheory produces consistent results for empty data
Code
result@mean_fit
Output
$truth
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
$average
[1] 0.9856020 0.9906587 0.9934492 0.9951504 0.9962639 0.9970324 0.9975852
[8] 0.9979962 0.9983101 0.9985553 0.9987506 0.9989086 0.9990382 0.9991460
[15] 0.9992365 0.9993133 0.9993790
$lower
[1] 0.9856020 0.9906587 0.9934492 0.9951504 0.9962639 0.9970324 0.9975852
[8] 0.9979962 0.9983101 0.9985553 0.9987506 0.9989086 0.9990382 0.9991460
[15] 0.9992365 0.9993133 0.9993790
$upper
[1] 0.9856020 0.9906587 0.9934492 0.9951504 0.9962639 0.9970324 0.9975852
[8] 0.9979962 0.9983101 0.9985553 0.9987506 0.9989086 0.9990382 0.9991460
[15] 0.9992365 0.9993133 0.9993790
NextBestInfTheory produces consistent results with a dataset
Code
result@mean_fit
Output
$truth
[1] 1 1 1 1 1 1 1 1 1 1 1 1
$average
[1] 0.1789688 0.2222795 0.2507550 0.2723040 0.2897450 0.3044386 0.3171541
[8] 0.3283720 0.3384141 0.3475067 0.3558159 0.3634667
$lower
[1] 0.1789688 0.2222795 0.2507550 0.2723040 0.2897450 0.3044386 0.3171541
[8] 0.3283720 0.3384141 0.3475067 0.3558159 0.3634667
$upper
[1] 0.1789688 0.2222795 0.2507550 0.2723040 0.2897450 0.3044386 0.3171541
[8] 0.3283720 0.3384141 0.3475067 0.3558159 0.3634667
examine produces consistent results
Code
result
Output
dose DLTs nextDose stop increment
1 3 0 1.0 FALSE -67
2 3 1 3.0 FALSE 0
3 3 2 1.0 FALSE -67
4 3 3 NA FALSE NA
5 1 0 1.0 FALSE 0
6 1 1 1.0 FALSE 0
7 1 2 1.0 FALSE 0
8 1 3 1.0 FALSE 0
9 1 0 1.0 FALSE 0
10 1 1 1.0 FALSE 0
11 1 2 1.0 FALSE 0
12 1 3 0.1 TRUE -90
13 1 0 1.0 FALSE 0
14 1 1 1.0 FALSE 0
15 1 2 1.0 TRUE 0
16 1 3 1.0 FALSE 0
17 1 0 1.0 FALSE 0
18 1 1 1.0 FALSE 0
19 1 2 1.0 FALSE 0
20 1 3 1.0 FALSE 0
21 1 0 1.0 FALSE 0
22 1 1 1.0 FALSE 0
23 1 2 1.0 FALSE 0
24 1 3 1.0 FALSE 0
25 1 0 1.0 TRUE 0
26 1 1 1.0 TRUE 0
27 1 2 1.0 TRUE 0
28 1 3 1.0 TRUE 0
examine produces consistent results with placebo data
Code
result
Output
dose DLTs nextDose stop increment
1 3 0 1 FALSE -67
2 3 1 3 FALSE 0
3 3 2 1 FALSE -67
4 3 3 NA FALSE NA
5 1 0 1 FALSE 0
6 1 1 1 FALSE 0
7 1 2 1 FALSE 0
8 1 3 1 FALSE 0
9 1 0 1 FALSE 0
10 1 1 1 FALSE 0
11 1 2 1 TRUE 0
12 1 3 1 FALSE 0
13 1 0 1 FALSE 0
14 1 1 1 FALSE 0
15 1 2 1 TRUE 0
16 1 3 1 FALSE 0
17 1 0 1 TRUE 0
18 1 1 1 TRUE 0
19 1 2 1 TRUE 0
20 1 3 1 TRUE 0
tidy-DualDesign works correctly
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simulate produces consistent results with placebo data
Code
result
Output
An object of class "Simulations"
Slot "fit":
[[1]]
middle lower upper
1 0.002380255 0.000638856 0.007082031
2 0.014222189 0.008233734 0.030391015
3 0.036277884 0.027439857 0.060140558
4 0.056826691 0.047465309 0.082102421
5 0.104528948 0.097325308 0.123978777
6 0.147987480 0.144820674 0.156537858
7 0.187713911 0.183810054 0.189159784
8 0.224084458 0.207488656 0.230231052
9 0.316307005 0.264631442 0.335446102
10 0.366443100 0.295038058 0.392889412
11 0.479544075 0.364837154 0.522028120
12 0.533826996 0.400048833 0.583374464
Slot "stop_report":
<NA> <NA> ≥ 3 cohorts dosed P(0.2 ≤ prob(DLE | NBD) ≤ 0.35) ≥ 0.5
[1,] TRUE FALSE TRUE FALSE
≥ 20 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[1] "Number of cohorts is 5 and thus reached the prespecified minimum number 3"
[[1]][[1]][[2]]
[1] "Probability for target toxicity is 0 % for dose 1 and thus below the required 50 %"
[[1]][[2]]
[1] "Number of patients is 20 and thus reached the prespecified minimum number 20"
Slot "additional_stats":
[[1]]
list()
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 0.1 3.0 3.0 3.0 0.1 1.0 1.0 1.0 0.1 1.0 1.0 1.0 0.1 1.0 1.0 1.0 0.1 1.0 1.0
[20] 1.0
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Slot "doseGrid":
[1] 0.1 1.0 3.0 5.0 10.0 15.0 20.0 25.0 40.0 50.0 80.0 100.0
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 1 3 3 3 1 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2
Slot "placebo":
[1] TRUE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Slot "cohort":
[1] 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5
Slot "nObs":
[1] 20
Slot "doses":
[1] 1
Slot "seed":
[1] 819
simulate produces consistent results with sentinel patients
Code
result
Output
An object of class "Simulations"
Slot "fit":
[[1]]
middle lower upper
1 0.002395946 0.0006466696 0.007118992
2 0.014321117 0.0083103333 0.030550234
3 0.036517541 0.0276538514 0.060449502
4 0.057182146 0.0477992422 0.082515987
5 0.105108928 0.0978987592 0.124576383
6 0.148728222 0.1455668979 0.157263798
7 0.188570939 0.1846333002 0.190029324
8 0.225025224 0.2083888362 0.231186850
9 0.317375596 0.2656888966 0.336518818
10 0.367536873 0.2961634016 0.393971492
11 0.480600330 0.3660795717 0.523015426
12 0.534826616 0.4013310812 0.584269407
Slot "stop_report":
<NA> <NA> ≥ 3 cohorts dosed P(0.2 ≤ prob(DLE | NBD) ≤ 0.35) ≥ 0.5
[1,] TRUE FALSE TRUE FALSE
≥ 20 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[1] "Number of cohorts is 7 and thus reached the prespecified minimum number 3"
[[1]][[1]][[2]]
[1] "Probability for target toxicity is 0 % for dose 1 and thus below the required 50 %"
[[1]][[2]]
[1] "Number of patients is 21 and thus reached the prespecified minimum number 20"
Slot "additional_stats":
[[1]]
list()
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Slot "doseGrid":
[1] 0.1 1.0 3.0 5.0 10.0 15.0 20.0 25.0 40.0 50.0 80.0 100.0
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7
Slot "nObs":
[1] 21
Slot "doses":
[1] 1
Slot "seed":
[1] 819
simulate-RuleDesign produces consistent results
Code
result
Output
An object of class "GeneralSimulations"
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 1 1 1 3 3 3 5 5 5 10 10 10 15 15 15 20 20 20 25 25 25 25 25 25 40
[26] 40 40 50 50 50 80 80 80 50 50 50
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
Slot "doseGrid":
[1] 1 3 5 10 15 20 25 40 50 80 100
Slot "nGrid":
[1] 11
Slot "xLevel":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 7 7 7 8
[26] 8 8 9 9 9 10 10 10 9 9 9
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 8 8 8 9
[26] 9 9 10 10 10 11 11 11 12 12 12
Slot "nObs":
[1] 36
Slot "doses":
[1] 50
Slot "seed":
[1] 819
Code
result
Output
dose DLTs nextDose stop increment
1 1 0 3 FALSE 200
2 1 1 1 FALSE 0
3 1 2 NA TRUE NA
4 1 3 NA TRUE NA
5 3 0 5 FALSE 67
6 3 1 3 FALSE 0
7 3 2 1 FALSE -67
8 3 3 1 FALSE -67
9 5 0 10 FALSE 100
10 5 1 5 FALSE 0
11 5 2 3 FALSE -40
12 5 3 3 FALSE -40
13 10 0 15 FALSE 50
14 10 1 10 FALSE 0
15 10 2 5 FALSE -50
16 10 3 5 FALSE -50
17 15 0 20 FALSE 33
18 15 1 15 FALSE 0
19 15 2 10 FALSE -33
20 15 3 10 FALSE -33
21 20 0 25 FALSE 25
22 20 1 20 FALSE 0
23 20 2 15 FALSE -25
24 20 3 15 FALSE -25
25 25 0 40 FALSE 60
26 25 1 25 FALSE 0
27 25 2 20 FALSE -20
28 25 3 20 FALSE -20
29 40 0 50 FALSE 25
30 40 1 40 FALSE 0
31 40 2 25 FALSE -38
32 40 3 25 FALSE -38
33 50 0 80 FALSE 60
34 50 1 50 FALSE 0
35 50 2 40 FALSE -20
36 50 3 40 FALSE -20
37 80 0 100 FALSE 25
38 80 1 80 FALSE 0
39 80 2 50 FALSE -38
40 80 3 50 FALSE -38
simulate-DualDesign produces consistent results
Code
result
Output
An object of class "DualSimulations"
Slot "rho_est":
[1] 0.07991541
Slot "sigma2w_est":
[1] 0.03177778
Slot "fit_biomarker":
[[1]]
middleBiomarker lowerBiomarker upperBiomarker
1 0.2434966 0.13294431 0.3701356
2 0.2325369 0.07576239 0.3763400
3 0.2284404 -0.07077652 0.5703187
4 0.2522588 -0.36780215 0.7826113
5 0.2364434 -0.49574988 0.9677055
6 0.2198298 -0.58828157 0.9583787
7 0.2185295 -0.71740079 1.1194549
8 0.2034642 -0.98358012 1.3289780
9 0.2058875 -1.12465077 1.4500549
10 0.1976203 -1.48546712 1.7065673
11 0.1905852 -1.80896626 2.0767703
Slot "fit":
[[1]]
middle lower upper
1 0.01093716 0.0009962211 0.04095732
2 0.22473302 0.0526930358 0.68081948
3 0.43571994 0.0935359919 0.97409485
4 0.67981427 0.1808020000 0.99996023
5 0.77864284 0.2500773718 0.99999984
6 0.82773116 0.3066616729 1.00000000
7 0.85641581 0.3540839546 1.00000000
8 0.89924516 0.4608241419 1.00000000
9 0.91428153 0.5130270841 1.00000000
10 0.93939515 0.6211946840 1.00000000
11 0.94903699 0.6699001621 1.00000000
Slot "stop_report":
<NA> P(0.9 ≤ Biomarker ≤ 1) ≥ 0.5 (relative) ≥ 10 patients dosed
[1,] TRUE FALSE TRUE
Slot "stop_reasons":
[[1]]
[[1]][[1]]
[1] "Probability for target biomarker is 21 % for dose 1 and thus below the required 50 %"
[[1]][[2]]
[1] "Number of patients is 12 and thus reached the prespecified minimum number 10"
Slot "additional_stats":
[[1]]
list()
Slot "data":
[[1]]
An object of class "DataDual"
Slot "w":
[1] 0.2557299 0.1150998 0.3181927 0.2531184 0.1632822 0.3616207 0.2672235
[8] 0.1000139 0.1305151 0.2393188 0.3006751 0.2951640
Slot "x":
[1] 3 3 3 1 1 1 1 1 1 1 1 1
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0
Slot "doseGrid":
[1] 1 3 5 10 15 20 25 40 50 80 100
Slot "nGrid":
[1] 11
Slot "xLevel":
[1] 2 2 2 1 1 1 1 1 1 1 1 1
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4
Slot "nObs":
[1] 12
Slot "doses":
[1] 1
Slot "seed":
[1] 3
simulate-TDSamplesDesign produces consistent results
Code
result
Output
An object of class "PseudoSimulations"
Slot "fit":
[[1]]
middle lower upper
1 0.08583626 0.02314306 0.1615121
2 0.13700263 0.05461354 0.2354364
3 0.17983349 0.08156022 0.2883433
4 0.21717502 0.10142184 0.3323753
5 0.25027602 0.12585805 0.3964506
6 0.27989888 0.14829847 0.4402591
7 0.30659033 0.15645905 0.4676890
8 0.33077494 0.16457914 0.5086352
9 0.35279588 0.16964809 0.5446742
10 0.37293640 0.17428698 0.5765315
11 0.39143316 0.17856908 0.6048191
12 0.40848551 0.18254982 0.6300498
Slot "final_td_target_during_trial_estimates":
[1] 169.4013
Slot "final_td_target_end_of_trial_estimates":
[1] 131.4969
Slot "final_td_target_during_trial_at_dose_grid":
[1] 150
Slot "final_td_target_end_of_trial_at_dose_grid":
[1] 125
Slot "final_tdeot_cis":
[[1]]
[[1]]$lower
[1] 81.58873
[[1]]$upper
[1] 1429.96
Slot "final_tdeot_ratios":
[1] 17.52644
Slot "final_cis":
[[1]]
[[1]]$lower
[1] 81.58873
[[1]]$upper
[1] 1429.96
Slot "final_ratios":
[1] 17.52644
Slot "stop_report":
≥ 36 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[1] "Number of patients is 36 and thus reached the prespecified minimum number 36"
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 25 25 25 50 50 50 75 75 75 100 100 100 125 125 125 300 300 300 75
[20] 75 75 125 125 125 150 150 150 150 150 150 225 225 225 150 150 150
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0
Slot "doseGrid":
[1] 25 50 75 100 125 150 175 200 225 250 275 300
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 12 12 12 3 3 3 5 5 5 6
[26] 6 6 6 6 6 9 9 9 6 6 6
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 8 8 8 9
[26] 9 9 10 10 10 11 11 11 12 12 12
Slot "nObs":
[1] 36
Slot "doses":
[1] 125
Slot "seed":
[1] 819
simulate-TDDesign produces consistent results
Code
result
Output
An object of class "PseudoSimulations"
Slot "fit":
[[1]]
[[1]]$phi1
[1] -9.791609
[[1]]$phi2
[1] 1.826935
[[1]]$probDLE
[1] 0.01962880 0.06632269 0.12967442 0.20128792 0.27476182 0.34581054
[7] 0.41195661 0.47204678 0.52579017 0.57340058 0.61535170 0.65222382
Slot "final_td_target_during_trial_estimates":
[1] 151.5239
Slot "final_td_target_end_of_trial_estimates":
[1] 133.7273
Slot "final_td_target_during_trial_at_dose_grid":
[1] 150
Slot "final_td_target_end_of_trial_at_dose_grid":
[1] 125
Slot "final_tdeot_cis":
[[1]]
[[1]]$lower
[1] 90.1733
[[1]]$upper
[1] 198.318
Slot "final_tdeot_ratios":
[1] 2.199298
Slot "final_cis":
[[1]]
[[1]]$lower
[1] 90.1733
[[1]]$upper
[1] 198.318
Slot "final_ratios":
[1] 2.199298
Slot "stop_report":
≥ 36 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[1] "Number of patients is 36 and thus reached the prespecified minimum number 36"
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 50 50 50 100 100 100 200 200 200 75 75 75 100 100 100 125 125 125 150
[20] 150 150 150 150 150 125 125 125 150 150 150 150 150 150 125 125 125
Slot "y":
[1] 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 1 0 0 0
Slot "doseGrid":
[1] 25 50 75 100 125 150 175 200 225 250 275 300
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 2 2 2 4 4 4 8 8 8 3 3 3 4 4 4 5 5 5 6 6 6 6 6 6 5 5 5 6 6 6 6 6 6 5 5 5
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 8 8 8 9
[26] 9 9 10 10 10 11 11 11 12 12 12
Slot "nObs":
[1] 36
Slot "doses":
[1] 125
Slot "seed":
[1] 819
simulate-DualResponsesDesign produces consistent results
Code
result
Output
An object of class "PseudoDualSimulations"
Slot "fitEff":
[[1]]
[[1]]$theta1
[1] -4.20662
[[1]]$theta2
[1] 3.302767
[[1]]$ExpEff
[1] -0.3455793 0.2985344 0.6242505 0.8372982 0.9935774 1.1159959
[7] 1.2160729 1.3003781 1.3729953 1.4366277 1.4931529 1.5439249
Slot "FinalGstarEstimates":
[1] 146.2479
Slot "FinalGstarAtDoseGrid":
[1] 125
Slot "FinalGstarCIs":
[[1]]
[[1]]$lower
[1] 75.03531
[[1]]$upper
[1] 285.0453
Slot "FinalGstarRatios":
[1] 3.798815
Slot "FinalOptimalDose":
[1] 137.5996
Slot "FinalOptimalDoseAtDoseGrid":
[1] 125
Slot "sigma2est":
[1] 0.1616952
Slot "fit":
[[1]]
[[1]]$phi1
[1] -9.998377
[[1]]$phi2
[1] 1.858333
[[1]]$probDLE
[1] 0.01769477 0.06131043 0.12184797 0.19147776 0.26390666 0.33471252
[7] 0.40119608 0.46198895 0.51662937 0.56521068 0.60812916 0.64591963
Slot "final_td_target_during_trial_estimates":
[1] 155.5827
Slot "final_td_target_end_of_trial_estimates":
[1] 137.5996
Slot "final_td_target_during_trial_at_dose_grid":
[1] 150
Slot "final_td_target_end_of_trial_at_dose_grid":
[1] 125
Slot "final_tdeot_cis":
[[1]]
[[1]]$lower
[1] 92.64601
[[1]]$upper
[1] 204.3655
Slot "final_tdeot_ratios":
[1] 2.205875
Slot "final_cis":
[[1]]
[[1]]$lower
[1] 92.64601
[[1]]$upper
[1] 204.3655
Slot "final_ratios":
[1] 2.205875
Slot "stop_report":
≥ 36 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[1] "Number of patients is 36 and thus reached the prespecified minimum number 36"
Slot "data":
[[1]]
An object of class "DataDual"
Slot "w":
[1] -0.6588212 -0.6574900 -0.6767438 0.4201327 0.1657673 0.6398290
[7] 1.0794245 1.0493776 0.9825006 1.6985308 1.7758098 1.5194703
[13] 1.1747540 0.7687320 0.5822763 0.8930588 1.0287153 0.9338591
[19] 1.1158580 1.0035394 1.0517295 1.5133967 1.2179947 1.0877295
[25] 1.6171994 1.2565239 1.0710927 0.9289380 0.8863580 0.8356701
[31] 0.7950787 0.6793477 0.7302074 0.7626097 0.8732485 0.8272182
Slot "x":
[1] 25 25 25 75 75 75 125 125 125 250 250 250 100 100 100 125 125 125 150
[20] 150 150 150 150 150 175 175 175 125 125 125 125 125 125 125 125 125
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0 0
Slot "doseGrid":
[1] 25 50 75 100 125 150 175 200 225 250 275 300
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 1 1 1 3 3 3 5 5 5 10 10 10 4 4 4 5 5 5 6 6 6 6 6 6 7
[26] 7 7 5 5 5 5 5 5 5 5 5
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 8 8 8 9
[26] 9 9 10 10 10 11 11 11 12 12 12
Slot "nObs":
[1] 36
Slot "doses":
[1] 125
Slot "seed":
[1] 819
simulate-DualResponsesSamplesDesign produces consistent results
Code
result
Output
An object of class "PseudoDualSimulations"
Slot "fitEff":
[[1]]
middle lower upper
1 -0.3079474 -0.8521410 0.08196096
2 0.3736494 0.1411489 0.55468787
3 0.7183200 0.5077815 0.88538501
4 0.9437657 0.7214829 1.15179652
5 1.1091393 0.8738198 1.41328216
6 1.2386817 0.9809589 1.61908158
7 1.3445825 1.0555782 1.78343868
8 1.4337937 1.1087482 1.89651747
9 1.5106368 1.1545466 1.99391914
10 1.5779722 1.1946786 2.07926954
11 1.6377868 1.2303281 2.15508685
12 1.6915134 1.2623492 2.22318756
Slot "FinalGstarEstimates":
[1] 300
Slot "FinalGstarAtDoseGrid":
[1] 225
Slot "FinalGstarCIs":
[[1]]
[[1]]$lower
[1] 300
[[1]]$upper
[1] 300
Slot "FinalGstarRatios":
[1] 1
Slot "FinalOptimalDose":
[1] 62.78087
Slot "FinalOptimalDoseAtDoseGrid":
[1] 50
Slot "sigma2est":
[1] 0.2648646
Slot "fit":
[[1]]
middle lower upper
1 0.1627044 0.07680748 0.2713562
2 0.2061350 0.09015426 0.3239337
3 0.2352925 0.09889961 0.3570465
4 0.2575780 0.10555513 0.3819918
5 0.2757086 0.11098665 0.4045258
6 0.2910230 0.11560460 0.4232458
7 0.3042901 0.11963849 0.4392495
8 0.3159958 0.12323056 0.4532166
9 0.3264687 0.12647556 0.4655986
10 0.3359419 0.12943997 0.4767113
11 0.3445874 0.13217237 0.4867840
12 0.3525358 0.13470940 0.4960901
Slot "final_td_target_during_trial_estimates":
[1] 95.95589
Slot "final_td_target_end_of_trial_estimates":
[1] 62.78087
Slot "final_td_target_during_trial_at_dose_grid":
[1] 75
Slot "final_td_target_end_of_trial_at_dose_grid":
[1] 50
Slot "final_tdeot_cis":
[[1]]
[[1]]$lower
[1] 62.78087
[[1]]$upper
[1] 62.78087
Slot "final_tdeot_ratios":
[1] 1
Slot "final_cis":
[[1]]
[[1]]$lower
[1] 62.78087
[[1]]$upper
[1] 62.78087
Slot "final_ratios":
[1] 1
Slot "stop_report":
≥ 10 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[1] "Number of patients is 12 and thus reached the prespecified minimum number 10"
Slot "data":
[[1]]
An object of class "DataDual"
Slot "w":
[1] -0.6588212 -0.6574900 -0.6767438 0.5106132 0.5020599 0.5558647
[7] 1.0105606 0.8984411 1.2273833 0.6454706 0.5818376 0.4685371
Slot "x":
[1] 25 25 25 75 75 75 125 125 125 75 75 75
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 0 0
Slot "doseGrid":
[1] 25 50 75 100 125 150 175 200 225 250 275 300
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 1 1 1 3 3 3 5 5 5 3 3 3
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12
Slot "cohort":
[1] 1 1 1 2 2 2 3 3 3 4 4 4
Slot "nObs":
[1] 12
Slot "doses":
[1] 50
Slot "seed":
[1] 819
Test if simulate generate the expected output.
Code
sim
Output
An object of class "Simulations"
Slot "fit":
[[1]]
middle lower upper
1 0.3185617 0.1610002 0.4380715
2 0.3115668 0.2305102 0.4260590
3 0.3123386 0.2023495 0.4190715
4 0.3148504 0.1839781 0.4141332
5 0.3177190 0.1706377 0.4250526
6 0.3205237 0.1603176 0.4615404
7 0.3231301 0.1519912 0.4927203
8 0.3255041 0.1450683 0.5197231
9 0.3276489 0.1391811 0.5433694
10 0.3295813 0.1340857 0.5642717
11 0.3313223 0.1296131 0.5828979
12 0.3328926 0.1256414 0.5996128
Slot "stop_report":
≥ 5 patients dosed
[1,] TRUE
Slot "stop_reasons":
[[1]]
[1] "Number of patients is 15 and thus reached the prespecified minimum number 5"
Slot "additional_stats":
[[1]]
list()
Slot "data":
[[1]]
An object of class "Data"
Slot "x":
[1] 25 25 25 25 50 50 50 50 100 100 100 100 25 25 25
Slot "y":
[1] 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1
Slot "doseGrid":
[1] 25 50 75 100 125 150 175 200 225 250 275 300
Slot "nGrid":
[1] 12
Slot "xLevel":
[1] 1 1 1 1 2 2 2 2 4 4 4 4 1 1 1
Slot "placebo":
[1] FALSE
Slot "ID":
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Slot "cohort":
[1] 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4
Slot "nObs":
[1] 15
Slot "doses":
[1] NA
Slot "seed":
[1] 819
NextBestInfTheory produces consistent results for empty data
Code
result@mean_fit
Output
$truth
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
$average
[1] 0.9856020 0.9906587 0.9934492 0.9951504 0.9962639 0.9970324 0.9975852
[8] 0.9979962 0.9983101 0.9985553 0.9987506 0.9989086 0.9990382 0.9991460
[15] 0.9992365 0.9993133 0.9993790
$lower
[1] 0.9856020 0.9906587 0.9934492 0.9951504 0.9962639 0.9970324 0.9975852
[8] 0.9979962 0.9983101 0.9985553 0.9987506 0.9989086 0.9990382 0.9991460
[15] 0.9992365 0.9993133 0.9993790
$upper
[1] 0.9856020 0.9906587 0.9934492 0.9951504 0.9962639 0.9970324 0.9975852
[8] 0.9979962 0.9983101 0.9985553 0.9987506 0.9989086 0.9990382 0.9991460
[15] 0.9992365 0.9993133 0.9993790
NextBestInfTheory produces consistent results with a dataset
Code
result@mean_fit
Output
$truth
[1] 1 1 1 1 1 1 1 1 1 1 1 1
$average
[1] 0.1789688 0.2222795 0.2507550 0.2723040 0.2897450 0.3044386 0.3171541
[8] 0.3283720 0.3384141 0.3475067 0.3558159 0.3634667
$lower
[1] 0.1789688 0.2222795 0.2507550 0.2723040 0.2897450 0.3044386 0.3171541
[8] 0.3283720 0.3384141 0.3475067 0.3558159 0.3634667
$upper
[1] 0.1789688 0.2222795 0.2507550 0.2723040 0.2897450 0.3044386 0.3171541
[8] 0.3283720 0.3384141 0.3475067 0.3558159 0.3634667
examine produces consistent results
Code
result
Output
dose DLTs nextDose stop increment
1 3 0 1.0 FALSE -67
2 3 1 3.0 FALSE 0
3 3 2 1.0 FALSE -67
4 3 3 NA FALSE NA
5 1 0 1.0 FALSE 0
6 1 1 1.0 FALSE 0
7 1 2 1.0 FALSE 0
8 1 3 1.0 FALSE 0
9 1 0 1.0 FALSE 0
10 1 1 1.0 FALSE 0
11 1 2 1.0 FALSE 0
12 1 3 0.1 TRUE -90
13 1 0 1.0 FALSE 0
14 1 1 1.0 FALSE 0
15 1 2 1.0 TRUE 0
16 1 3 1.0 FALSE 0
17 1 0 1.0 FALSE 0
18 1 1 1.0 FALSE 0
19 1 2 1.0 FALSE 0
20 1 3 1.0 FALSE 0
21 1 0 1.0 FALSE 0
22 1 1 1.0 FALSE 0
23 1 2 1.0 FALSE 0
24 1 3 1.0 FALSE 0
25 1 0 1.0 TRUE 0
26 1 1 1.0 TRUE 0
27 1 2 1.0 TRUE 0
28 1 3 1.0 TRUE 0
examine produces consistent results with placebo data
Code
result
Output
dose DLTs nextDose stop increment
1 3 0 1 FALSE -67
2 3 1 3 FALSE 0
3 3 2 1 FALSE -67
4 3 3 NA FALSE NA
5 1 0 1 FALSE 0
6 1 1 1 FALSE 0
7 1 2 1 FALSE 0
8 1 3 1 FALSE 0
9 1 0 1 FALSE 0
10 1 1 1 FALSE 0
11 1 2 1 TRUE 0
12 1 3 1 FALSE 0
13 1 0 1 FALSE 0
14 1 1 1 FALSE 0
15 1 2 1 TRUE 0
16 1 3 1 FALSE 0
17 1 0 1 TRUE 0
18 1 1 1 TRUE 0
19 1 2 1 TRUE 0
20 1 3 1 TRUE 0
tidy-DualDesign works correctly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