pa.ABE | 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 ABE if these values deviate from the ones assumed in planning the sample size of the study.

pa.ABE(CV, theta0 = 0.95, targetpower = 0.8, minpower = 0.7, design = "2x2", ...) ## S3 method for class 'pwrA' print(x, digits = 4, plotit = TRUE, ...) ## S3 method for class 'pwrA' plot(x, pct = TRUE, ratiolabel = "theta0", cols = c("blue", "red"), ...)

`CV` |
Coefficient of variation 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. |

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

Additional arguments of the S3 methods:

`x` |
Object of class |

`digits` |
Digits for rounding power in printing. The '...' argument is currently ignored
in |

`plotit` |
If set to |

`pct` |
If set to |

`ratiolabel` |
Label of the T/R-ratio. Can be set to any string, e.g. to |

`cols` |
Colors for the plots. |

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

and calculations of CV and theta0
which gave a power=`minpower`

are derived via R base `uniroot`

.
While one of the parameters (`CV`

, `theta0`

, `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.

It should be kept in mind 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.
See output of |

`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 "ABE". |

`minpower` |
Minimum acceptable power. |

The class `'pwrA'`

has the S3 methods `print()`

and `plot()`

.
See `pa.scABE`

for usage.

The code 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.TOST, known.designs, pa.scABE, pa.NTIDFDA`

# using the defaults # design="2x2", targetpower=0.8, minpower=0.7, theta0/GMR=0.95 # BE margins from defaults of sampleN.TOST() 0.8 ... 1.25 # print & plot implicitly pa.ABE(CV = 0.2) # print & plot res <- pa.ABE(CV = 0.2) print(res, plotit = FALSE) # print only plot(res, pct = FALSE, ratiolabel = "GMR") # changed from defaults

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