pa.scABE | R Documentation |

An analysis tool for exploration/visualization of the impact of expected values (CV, theta0, reduced sample size due to drop-outs) on power of BE decision via scABE (for highly variable drugs) if these values deviate from the ones assumed in planning the sample size of the study.

pa.scABE(CV, theta0 = 0.9, targetpower = 0.8, minpower = 0.7, design = c("2x3x3", "2x2x4", "2x2x3"), regulator = c("EMA", "HC", "FDA", "GCC"), ...)

`CV` |
Coefficient of variation of the intra-subject variability as ratio (not percent). |

`theta0` |
‘True’ or assumed T/R ratio. Often named GMR. |

`targetpower` |
Power to achieve at least in sample size estimation. Must be >0 and <1. |

`minpower` |
Minimum acceptable power to have if deviating from assumptions for sample size plan. |

`design` |
Character string describing the study design. |

`regulator` |
Character string describing the scaled ABE method recommended by the regulatory
bodies |

`...` |
More arguments to pass to |

Power calculations are done via `power.scABEL()`

or `power.RSABE()`

and
calculations of CV and theta0 which result in `minpower`

derived via R base `uniroot`

.

While one of the parameters (CV, GMR, N) is varied, the respective two others are
kept constant. The tool shows the relative impact of single parameters on power.

The tool takes a minimum of 12 subjects as required in most BE guidances into account.
However, the sample size will be increased from the estimated one if one of the
following conditions is applicable:

The FDA requires at least 24 subjects

*enrolled*in studies intended for reference-scaling.The EMA requires at least 12

*eligible*subjects in the sequence RTR of the TRT|RTR-design (hence the minimum sample size is 24).

You should be aware that this is **not** a substitute for the “Sensitivity Analysis”
recommended in ICH-E9. 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.

Returns a list with class `'pwrA'`

with the components

`plan` |
A data.frame with the result of the sample size estimation. |

`paCV` |
A data.frame with value pairs CV, pwr for impact of deviations from CV. |

`paGMR` |
A data.frame with value pairs theta0, pwr for impact of deviations from theta0 (GMR). |

`paN` |
A data.frame with value pairs N, pwr for impact of deviations from planned N (dropouts). |

`method` |
Method of BE decision. Here fix = "scABE". |

`regulator` |
"EMA", "HC", or "FDA". |

`minpower` |
Minimum acceptable power from the call of the function. |

The class `'pwrA'`

has the S3 methods `print()`

and `plot()`

.
See `pa.ABE`

for usage.

The code for impact of deviations from planned sample size tries to keep the degree of imbalance as low as possible between (sequence) groups. This results in a lesser decrease of power than more extreme dropout-patterns.

Idea and original code by H. Schütz with modifications by D. Labes to use PowerTOST infrastructure.

Schütz H. *Deviating from assumptions.* August 08, 2014.
BEBA Forum

```
power.scABEL, power.RSABE, known.designs,
print.pwrA, plot.pwrA, pa.ABE, pa.NTIDFDA
```

# Implicitely using the defaults: # design = "2x3x3", targetpower = 0.8, minpower = 0.7, # theta0 = 0.9, GMR = 0.90, regulator = "EMA" # widened BE margins from defaults of sampleN.scABEL() 0.7462 ... 1.3402 # 1E5 sims in power.scABEL() # not run due to timing policy of CRAN, may run some ten seconds # Implicit print & plot pa.scABE(CV = 0.4)

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