Note that analysis of untransformed data (`logscale = FALSE`

) is not supported. The terminology of the `design`

argument follows this pattern: `treatments x sequences x periods`

.

`pa.ABE()`

Parameter | Argument | Purpose | Default |
---|---|---|---|

CV |
`CV` |
CV | none |

\(\theta_{0}\) | `theta0` |
‘True’ or assumed deviation of T from R | `0.95` |

\(\pi\) | `targetpower` |
Minimum desired power | `0.80` |

\(\pi\) | `minpower` |
Minimum acceptable power | `0.70` |

design | `design` |
Planned design | `"2x2x2"` |

passed | `...` |
Arguments to `power.TOST()` |
none |

If no addtional 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 percent.

The *CV* is generally the *within*-subject coefficient of variation. Only 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.

With `x <- pa.ABE(...)`

results are given as an S3-object which can be printed, plotted, or both.

`pa.scABE()`

Parameter | Argument | Purpose | Default |
---|---|---|---|

CV |
`CV` |
CV | none |

\(\theta_{0}\) | `theta0` |
‘True’ or assumed deviation of T from R | `0.90` |

\(\pi\) | `targetpower` |
Minimum desired power | `0.80` |

\(\pi\) | `minpower` |
Minimum acceptable power | `0.70` |

design | `design` |
Planned replicate design | `"2x2x3"` |

regulator | `regulator` |
‘target’ jurisdiction | `"EMA"` |

nsims | `nsims` |
Number of simultions | `1e5` |

passed | `...` |
Arguments to `power.scABEL()` or `power.RSABE()` |
none |

If no addtional arguments are passed, the defaults of `power.scABEL()`

and `power.RSABE()`

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 percent. The *CV* is the *within*-subject coefficient of variation, where only homoscedasticity (*CV _{wT}* =

With `x <- pa.scABE(...)`

results are given as an S3-object which can be printed, plotted, or both.

`pa.NTIDFDA()`

Parameter | Argument | Purpose | Default |
---|---|---|---|

CV |
`CV` |
CV | none |

\(\theta_{0}\) | `theta0` |
‘True’ or assumed deviation of T from R | `0.975` |

\(\pi\) | `targetpower` |
Minimum desired power | `0.80` |

\(\pi\) | `minpower` |
Minimum acceptable power | `0.70` |

design | `design` |
Planned replicate design | `"2x2x4"` |

nsims | `nsims` |
Number of simultions | `1e5` |

passed | `...` |
Arguments to `power.NTIDFDA()` |
none |

If no addtional arguments are passed, the defaults of `power.NTIDFDA()`

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 percent. The *CV* is the *within*-subject coefficient of variation, where only homoscedasticity (*CV _{wT}* =

With `x <- pa.NTIDFDA(...)`

results are given as an S3-object which can be printed, plotted, or both.

Example 3 of vignette ABE. Assumed \(\theta_{0}\) 0.92, *CV* 0.20.

```
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 which lead to min. acceptable power of at least 0.7:
# CV= 0.2377, theta0= 0.9001
# n = 21 (power= 0.7104)
```

The most critical parameter is \(\theta_{0}\), whereas dropouts are the least important. We will see such a pattern in other approaches as well.

Assumed intra-subject *CV* 0.55 (*CV _{wT}* =

```
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 which lead to min. acceptable power of at least 0.7:
# CV= 0.6668, theta0= 0.8689
# n = 33 (power= 0.7085)
```

The idea behing 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

Assumed intra-subject *CV* 0.40 (*CV _{wT}* =

```
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 which lead to min. acceptable power of at least 0.7:
# CV= 0.6938, theta0= 0.8763
# n = 23 (power= 0.7123)
```

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 (30%) power increases again. Although we cannot scale anymore, with 30 subjects the study is essentially ‘overpowered’ for ABE.

Same data like in Example 2.

```
pa.scABE(CV = 0.55, regulator = "HC")
# Sample size plan scABE (HC/ABEL2)
# 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 which lead to min. acceptable power of at least 0.7:
# CV= 0.7599, theta0= 0.8647
# n = 30 (power= 0.7125)
```

Since we are close to Health Canada’s upper cap of 57.4%, power drops on both sides. Note that we need three subjects less than for the EMA’s method 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.

Same data like in Example 2 and Example 4.

```
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 which lead to min. acceptable power of at least 0.7:
# CV= 0.9713, theta0= 0.8652
# n = 23 (power= 0.7001)
```

A similar pattern like the one of Health Canada, although due to the different regulatory constant we need nine subjects less (and twelve less than for the EMA). Caused by unlimited scaling the *CV _{wR}* can increase more.

Assumed intra-subject *CV* 0.125.

```
pa.NTIDFDA(CV = 0.125)
# Sample size plan RSABE 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 which lead to min. acceptable power of at least 0.7:
# CV = (0.0716, 0.3059), theta0= 0.9569
# n = 13 (power= 0.7064)
```

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 addtional criterion ‘must pass conventional BE limits of 80.00–125.00%’ cuts in.

The power analysis is not a substitute for the ‘Sensitivity Analysis’ recommended by the ICH.^{1} In a real study a combination of all effects occurs simultaneously. It is up to *you* to decide on reasonable combinations and analyze their respective power.