Description Usage Arguments Details Value Note Author(s) References Examples

View source: R/SampleN2.TOST.R

This function estimates the necessary sample size of stage 2 to have at least a given power.

1 2 3 | ```
sampleN2.TOST(alpha = 0.0294, CV, n1, theta0 = 0.95,
theta1 = 0.8, theta2 = 1.25, targetpower = 0.8,
design = "2x2", method = "exact", imax = 100)
``` |

`alpha` |
Alpha value for the final analysis of pooled data. Defaults to Pocock’s
alpha setting |

`CV` |
Coefficient of variation of the intra-subject variability as ratio. |

`n1` |
Sample size of stage 1. |

`theta0` |
True ratio of T/R for simulating. Defaults to 0.95 argument if missing. |

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

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

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

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

`method` |
Method for calculation of the power. Implemented are |

`imax` |
Maximum number of steps in sample size search. |

The sample size is calculated via iterative evaluation of power of the
TOST procedure.

Start value for the sample size search is taken from a large sample
approximation according to Zhang, modified.

A data.frame with the input and results will be returned.

The "Sample size" column contains the sample size for the second stage.

Since in the final analysis one degree of freedom less than in a fixed sample
design (or in stage 1) is used, power will
be slightly lower than the one obtained with function `sampleN.TOST`

of
package `PowerTOST`

. However, different *sample sizes* are extremely
unlikely.

H. Schütz, D. Labes

Potvin D, DiLiberti CE, Hauck WW, Parr AF, Schuirmann DJ, Smith RA. *Sequential design approaches for bioequivalence studies with crossover designs.*

Pharm Stat. 2008; 7(4):245–62. doi: 10.1002/pst.294

Zhang P. *A Simple Formula for Sample Size Calculation in Equivalence Studies.*

J Biopharm Stat. 2003; 13(3):529–38. doi: 10.1081/BIP-120022772

1 2 3 4 5 6 7 | ```
# using all the defaults, CV of 25% and 12 subjects in stage 1
print(sampleN2.TOST(CV=0.25, n1=12), row.names=FALSE)
# should give a stage 2 sample of 22 and achieved power ~0.814
# CV 10% and 12 subjects in stage 1
print(sampleN2.TOST(CV=0.1, n1=12), row.names=FALSE)
# should give a sample size of 0 (second stage not reasonable
# since power ~0.973 already achieved in stage 1
``` |

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