data_2x4x4.RdThese data.frames give sample size tables calculated with
sampleN.TOST() for the 2×4×4 replicate crossover design
(2-treatment 4-sequence 4-period design).
The data.frames can be accessed by their names.
| data.frame | Description |
| ct9.6.4 | Additive model, theta1=–0.2, theta2=+0.2 (BE limits 0.80 – 1.20) |
| approximate power via shifted non-central t-distribution | |
| ct9.6.8 | Multiplicative model, theta1=0.8, theta2=1.25 (1/theta1) |
| approximate power via shifted non-central t-distribution |
Attention! CV is se (standard error) of residuals.
| data.frame | Origin | Details |
| ct9.6.4 | Chow & Liu | Table 9.6.4 (p 294) |
| ct9.6.8 | Chow & Liu | Table 9.6.8 (p 298) |
Chow SC, Liu JP. Design and Analysis of Bioavailability and Bioequivalence Studies. Boca Raton: CRC Press; 3rd edition 2009.
Scripts for creation of these data.frames can be found in the /tests
sub-directory of the package.
Comparing the results of these scripts to the corresponding data.frames can
be used for validation purposes.
ct9.6.4
#> CV power R0.0 R0.05 R0.1 R0.15
#> 1 0.10 0.8 4 4 8 28
#> 2 0.12 0.8 4 8 12 40
#> 3 0.14 0.8 8 8 16 52
#> 4 0.16 0.8 8 8 20 64
#> 5 0.18 0.8 8 12 24 84
#> 6 0.20 0.8 12 12 28 100
#> 7 0.22 0.8 12 16 32 124
#> 8 0.24 0.8 16 20 40 144
#> 9 0.26 0.8 16 20 44 168
#> 10 0.28 0.8 20 24 52 196
#> 11 0.30 0.8 20 28 60 224
#> 12 0.32 0.8 24 32 64 256
#> 13 0.34 0.8 28 36 72 288
#> 14 0.36 0.8 32 40 84 324
#> 15 0.38 0.8 32 44 92 360
#> 16 0.40 0.8 36 48 100 400
#> 17 0.10 0.9 4 8 12 36
#> 18 0.12 0.9 8 8 16 52
#> 19 0.14 0.9 8 12 20 68
#> 20 0.16 0.9 8 12 24 92
#> 21 0.18 0.9 12 16 32 112
#> 22 0.20 0.9 12 16 36 140
#> 23 0.22 0.9 16 20 44 168
#> 24 0.24 0.9 20 24 52 200
#> 25 0.26 0.9 20 28 60 236
#> 26 0.28 0.9 24 32 68 272
#> 27 0.30 0.9 28 36 80 312
#> 28 0.32 0.9 32 40 92 352
#> 29 0.34 0.9 32 48 100 400
#> 30 0.36 0.9 36 52 112 448
#> 31 0.38 0.9 40 56 128 496
#> 32 0.40 0.9 44 64 140 552
ct9.6.8
#> CV power R0.85 R0.9 R0.95 R1.0 R1.05 R1.1 R1.15 R1.2
#> 1 0.1002505 0.8 20 8 4 4 4 8 12 40
#> 2 0.1204333 0.8 28 8 4 4 4 8 16 56
#> 3 0.1406888 0.8 36 12 8 8 8 12 20 76
#> 4 0.1610295 0.8 44 12 8 8 8 12 24 96
#> 5 0.1814679 0.8 56 16 8 8 8 16 32 124
#> 6 0.2020168 0.8 68 20 12 8 12 16 40 152
#> 7 0.2226890 0.8 84 24 12 12 12 20 44 184
#> 8 0.2434978 0.8 100 28 16 12 16 24 52 216
#> 9 0.2644565 0.8 116 32 16 16 16 28 64 252
#> 10 0.2855787 0.8 136 36 20 16 20 32 72 292
#> 11 0.3068783 0.8 152 44 20 20 20 36 84 336
#> 12 0.3283695 0.8 176 48 24 20 24 40 92 384
#> 13 0.3500668 0.8 196 56 28 24 28 48 104 432
#> 14 0.3719851 0.8 220 60 32 24 28 52 116 484
#> 15 0.3941398 0.8 244 68 32 28 32 56 132 540
#> 16 0.4165464 0.8 272 72 36 32 36 64 144 596
#> 17 0.1002505 0.9 24 8 4 4 4 8 16 52
#> 18 0.1204333 0.9 36 12 8 8 8 12 20 76
#> 19 0.1406888 0.9 48 16 8 8 8 12 28 104
#> 20 0.1610295 0.9 64 20 12 8 8 16 36 136
#> 21 0.1814679 0.9 80 24 12 8 12 20 44 168
#> 22 0.2020168 0.9 96 28 16 12 12 24 52 208
#> 23 0.2226890 0.9 116 32 16 12 16 28 64 252
#> 24 0.2434978 0.9 136 40 20 16 20 32 72 300
#> 25 0.2644565 0.9 160 44 24 16 20 36 84 348
#> 26 0.2855787 0.9 184 52 24 20 24 44 100 404
#> 27 0.3068783 0.9 212 60 28 24 28 48 112 464
#> 28 0.3283695 0.9 240 64 32 24 32 56 128 528
#> 29 0.3500668 0.9 272 72 36 28 36 64 144 596
#> 30 0.3719851 0.9 304 84 40 32 40 72 164 668
#> 31 0.3941398 0.9 340 92 44 36 44 80 180 744
#> 32 0.4165464 0.9 376 100 48 36 48 88 200 824