Function pa.ABE()

If no additional arguments are passed, the defaults of power.TOST() are applied, namely alpha = 0.05, theta1 = 0.80, theta2 = 1.25.

Arguments targetpower, minpower, theta0, theta1, theta2, and CV have to be given as fractions, not in percent.
CV is generally the within- (intra-) subject coefficient of variation. In replicate designs only homoscedasticity (CV_wT = CV_wR) is supported. For design = "parallel" it is the total (a.k.a. pooled) CV.

The conventional TR|RT (a.k.a. AB|BA) design can be abbreviated as "2x2". Some call the "parallel" design a ‘one-sequence’ design. The "paired" design has two periods but no sequences, e.g., in studying linear pharmacokinetics a single dose is followed by multiple doses. A profile in steady state (T) is compared to the one after the single dose (R). Note that the underlying model assumes no period effects.

Function pa.scABE()

If no additional arguments are passed, the defaults of power.scABEL() and power.RSABE() are applied, namely alpha = 0.05, theta1 = 0.80, theta2 = 1.25. Note the recommended ² default $\small{\theta_0}$ 0.90 for HVDPs.
regulator can be "EMA", "HC", "GCC", or "FDA".

Arguments targetpower, minpower, theta0, theta1, theta2, and CV have to be given as fractions, not in percent. CV is the within- (intra-) subject coefficient of variation, where only homoscedasticity (CV_wT = CV_wR) is supported.

Function pa.NTIDFDA()

If no additional arguments are passed, the defaults of power.NTID() are applied, namely alpha = 0.05, theta1 = 0.80, theta2 = 1.25. Note the default $\small{\theta_0}$ 0.975 for NTIDs since the FDA requires tighter batch release limits of ±5% for them.

Arguments targetpower, minpower, theta0, theta1, theta2, and CV have to be given as fractions, not in percent. CV is the within- (intra-) subject coefficient of variation, where only homoscedasticity (CV_wT = CV_wR) is supported.

Average Bioequivalence

Example 1

Example 3 of vignette ABE. Assumed CV 0.20, $\small{\theta_0}$ 0.92.

pa.ABE(CV = 0.20, theta0 = 0.92)
# Sample size plan ABE
#  Design alpha  CV theta0 theta1 theta2 Sample size Achieved power
#     2x2  0.05 0.2   0.92    0.8   1.25          28       0.822742
# 
# Power analysis
# CV, theta0 and number of subjects leading to min. acceptable power of ~0.7:
#  CV= 0.2377, theta0= 0.9001
#  n = 21 (power= 0.7104)

Fig. 1 Power Analysis (in each panel one argument is varied and others kept constant)

The most critical parameter is $\small{\theta_0}$, whereas dropouts are the least important. We will see a similar pattern in other approaches as well.

Reference-scaled Average Bioequivalence

Highly Variable Drugs / Drug Products

EMA

Example 2

Assumed intra-subject CV 0.55.

pa.scABE(CV = 0.55)
# Sample size plan scABE (EMA/ABEL)
#  Design alpha CVwT CVwR theta0 theta1 theta2 Sample size Achieved power
#   2x3x3  0.05 0.55 0.55    0.9    0.8   1.25          42        0.80848
#  Target power
#           0.8
# 
# Power analysis
# CV, theta0 and number of subjects leading to min. acceptable power of ~0.7:
#  CV= 0.6668, theta0= 0.8689
#  n = 33 (power= 0.7085)

Fig. 2 Power Analysis (in each panel one argument is varied and others kept constant)

The idea behind reference-scaling is to preserve power even for high variability without requiring extreme sample sizes. However, we make two interesting observations. At CV_wR 0.55 already the upper cap of scaling (50%) cuts in and the expanded limits are the same as at CV_wR 0.50. Therefore, if the variability increases, power decreases. On the other hand, if the CV decreases, power increases first (because being affected by the upper cap is less likely) and then decreases again (because the limits can be less expanded).

Example 3

Assumed intra-subject CV 0.40, 4-period full replicate design.

pa.scABE(CV = 0.40, design = "2x2x4")
# Sample size plan scABE (EMA/ABEL)
#  Design alpha CVwT CVwR theta0 theta1 theta2 Sample size Achieved power
#   2x2x4  0.05  0.4  0.4    0.9    0.8   1.25          30        0.80656
#  Target power
#           0.8
# 
# Power analysis
# CV, theta0 and number of subjects leading to min. acceptable power of ~0.7:
#  CV= 0.6938, theta0= 0.8763
#  n = 23 (power= 0.7123)

Fig. 3 Power Analysis (in each panel one argument is varied and others kept constant)

Here we see a different pattern. With increasing variability power increases (due to more expanding) up to the cap of scaling where it starts to decrease like in the previous example. If the variability decreases, power decreases as well (less expanding). However, close the the switching CV_wR (30%) power increases again. Although we cannot scale anymore, with 30 subjects the study is essentially ‘overpowered’ for ABE.

Health Canada

Example 4

Same assumptions (CV, $\small{\theta_0}$) like in Example 2.

pa.scABE(CV = 0.55, regulator = "HC")
# Sample size plan scABE (HC/ABEL)
#  Design alpha CVwT CVwR theta0 theta1 theta2 Sample size Achieved power
#   2x3x3  0.05 0.55 0.55    0.9    0.8   1.25          39        0.81418
#  Target power
#           0.8
# 
# Power analysis
# CV, theta0 and number of subjects leading to min. acceptable power of ~0.7:
#  CV= 0.7599, theta0= 0.8647
#  n = 30 (power= 0.7125)

Fig. 4 Power Analysis (in each panel one argument is varied and others kept constant)

Since we are close to Health Canada’s upper cap of 57.4%, power decreases on both sides. Note that three subjects less than for the EMA’s method are required and CV_wR can increase to ~0.76 until we reach the minimum acceptable power – which is substantially higher than the ~0.67 for the EMA.

Gulf Cooperation Council

Example 5

Same assumptions (CV, $\small{\theta_0}$) like in Example 2 and Example 4.

pa.scABE(CV = 0.55, regulator = "GCC")
# Sample size plan scABE (GCC/ABEL)
#  Design alpha CVwT CVwR theta0 theta1 theta2 Sample size Achieved power
#   2x3x3  0.05 0.55 0.55    0.9    0.8   1.25          75        0.80214
#  Target power
#           0.8
# 
# Power analysis
# CV, theta0 and number of subjects leading to min. acceptable power of ~0.7:
#  CV= 0.6473, theta0= 0.8786
#  n = 57 (power= 0.7018)

Fig. 5 Power Analysis (in each panel one argument is varied and others kept constant)

Since we are far above the switching CV_wR of 30%, (fixed) widened BE margins of 75.00–133.33% are applicable and the pattern is similar to ABE.

FDA, CDE

Example 6

Same assumptions (CV, $\small{\theta_0}$) like in Example 2, Example 4 and Example 5.

pa.scABE(CV = 0.55, regulator = "FDA")
# Sample size plan scABE (FDA/RSABE)
#  Design alpha CVwT CVwR theta0 theta1 theta2 Sample size Achieved power
#   2x3x3  0.05 0.55 0.55    0.9    0.8   1.25          30        0.80034
#  Target power
#           0.8
# 
# Power analysis
# CV, theta0 and number of subjects leading to min. acceptable power of ~0.7:
#  CV= 0.9713, theta0= 0.8652
#  n = 23 (power= 0.7001)

Fig. 6 Power Analysis (in each panel one argument is varied and others kept constant)

A similar pattern like the one of Health Canada, although due to the different regulatory constants ($\small{\theta_s\approx0.8926}$ vs $\small{k=0.760}$ for Health Canada and the EMA) nine subjects less (and twelve less than for the EMA) are required. Due to unlimited scaling the CV can increase more.

Narrow Therapeutic Index Drugs (FDA, CDE)

Example 7

Assumed intra-subject CV 0.125.

pa.NTID(CV = 0.125)
# Sample size plan NTID
#  Design alpha  CVwT  CVwR theta0 theta1 theta2 Sample size Achieved power
#   2x2x4  0.05 0.125 0.125  0.975    0.8   1.25          16        0.82278
#  Target power
#           0.8
# 
# Power analysis
# CV, theta0 and number of subjects leading to min. acceptable power of ~0.7:
#  CV = (0.0716, 0.3059), theta0= 0.9569
#  n = 13 (power= 0.7064)

Fig. 7 Power Analysis (in each panel one argument is varied and others kept constant)

With decreasing variability power decreases because the scaled limits become narrower. With increasing variability we gain power because we have to scale less until at ~21.4% the additional criterion “must pass conventional BE limits of 80.00–125.00%” cuts in.