Description Usage Arguments Details Value Author(s) References See Also Examples

Following the design scheme according to `power.tsd.in`

the function
performs the analysis after the second stage has been performed.

1 2 3 |

`alpha` |
If one element is given, the overall one-sided significance level (not the
adjusted level for stage 2). If two
elements are given, the adjusted one-sided alpha levels for
stage 1 and
stage 2, respectively. |

`weight` |
Pre-defined weight(s) of stage 1.
Note that using the notation from Maurer et al weight corresponds to
information fraction, other literature may refer to sqrt(weight) as
being the weight. |

`max.comb.test` |
Logical; if |

`GMR1` |
Observed ratio of geometric means (T/R) of stage 1 data. |

`CV1` |
Observed coefficient of variation of the intra-subject variability of
stage 1 (use |

`n1` |
Sample size of stage 1. |

`df1` |
Optional; Error degrees of freedom of
stage 1 that can be specified in
addition to |

`SEM1` |
Optional; Standard error of the difference of means of
stage 1 that can be specified in
addition to |

`GMR2` |
Observed ratio of geometric means (T/R) of (only) stage 2 data. |

`CV2` |
Observed coefficient of variation of the intra-subject variability of (only)
stage 2 (use |

`n2` |
Sample size of stage 2. |

`df2` |
Optional; Error degrees of freedom of (only)
stage 2 that can be specified in
addition to |

`SEM2` |
Optional; Standard error of the difference of means of (only)
stage 2 that can be specified in
addition to |

`theta1` |
Lower bioequivalence limit. Defaults to 0.8. |

`theta2` |
Upper bioequivalence limit. Defaults to 1.25. |

The observed values (`GMR1`

, `GMR2`

etc.) should be obtained for
each stage separately; this may be done via the usual ANOVA approach.

The optional arguments `df1`

, `SEM1`

, `df2`

and `SEM2`

require a somewhat advanced knowledge (provided in the raw output from for
example the software SAS, or may be obtained via `emmeans::emmeans`

).
However, it has the advantage that if there were missing data the exact
degrees of freedom and standard error of the difference can be used,
the former possibly being non-integer valued (e.g. if the
Kenward-Roger method was used).

Returns an object of class `"evaltsd"`

with all the input arguments and results
as components. As part of the input arguments a component `cval`

is also
presented, containing the critical values for stage 1 and 2 according to the
input based on `alpha`

, `weight`

and `max.comb.test`

.

The class `"evaltsd"`

has an S3 print method.

The results are in the components:

`z1` |
Combination test statistic for first null hypothesis (standard
combination test statistic in case of |

`z2` |
Combination test statistic for second null hypothesis (standard
combination test statistic in case of |

`RCI` |
(Exact) repeated confidence interval for stage 2. |

`MEUE` |
Median unbiased estimate as estimate for the overall geometric mean ratio |

`stop_BE` |
Logical, indicating whether BE can be concluded after stage 2 or not. |

B. Lang

Patterson SD, Jones B. *Bioequivalence and Statistics in Clinical Pharmacology.*

Boca Raton: CRC Press; 2^{nd} edition 2017.

Maurer W, Jones B, Chen Y. *Controlling the type 1 error rate in two-stage
sequential designs when testing for average bioequivalence.*

Stat Med. 2018;1–21. doi: 10.1002/sim.7614

Wassmer G, Brannath W. *Group Sequential and Confirmatory Adaptive Designs
in Clinical Trials.*

Springer 2016. doi: 10.1007/978-3-319-32562-0

1 2 3 4 5 6 | ```
# Example from Maurer et al.
## Not run:
# Not run due to CRAN policy for run-time of examples
final.tsd.in(GMR1 = exp(0.0424), CV1 = 0.3682, n1 = 20,
GMR2 = exp(-0.0134), CV2 = 0.3644, n2 = 36)
## End(Not run)
``` |

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