sampleN.RatioF | R Documentation |

Estimates the necessary sample size to have at least a given power based on Fieller’s confidence (‘fiducial’) interval.

sampleN.RatioF(alpha = 0.025, targetpower = 0.8, theta1 = 0.8, theta2, theta0 = 0.95, CV, CVb, design = "2x2", print = TRUE, details = FALSE, imax=100, setseed=TRUE)

`alpha` |
Type I error probability. |

`targetpower` |
Power to achieve at least. Must be >0 and <1. Typical values are 0.8 or 0.9. |

`theta1` |
Lower bioequivalence limit. Typically 0.8 (default). |

`theta2` |
Upper bioequivalence limit. Typically 1.25. |

`theta0` |
‘True’ or assumed T/R ratio. Typically set to 0.95. |

`CV` |
Coefficient of variation as ratio. In case of |

`CVb` |
CV of the between-subject variability. Only necessary for |

`design` |
A character string describing the study design. |

`print` |
If |

`details` |
If |

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

`setseed` |
If set to |

The sample size is based on exact power calculated using the bivariate
non-central *t*-distribution via function `pmvt`

of the package `mvtnorm`

.

Due to the calculation method used in package mvtnorm these
probabilities are dependent from the state of the random number generator
within the precision of the power.

The CV(within) and CVb(etween) in case of `design="2x2"`

are obtained
via an appropriate ANOVA from the error term and from the difference
`(MS(subject within sequence)-MS(error))/2`

.

The estimated sample size gives always the *total* number of subjects (not subject/sequence in crossovers or subjects/group in parallel designs – like in some other software packages).

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

The sample size n returned is the total sample size for both designs.

This function is intended for studies with clinical endpoints.

In such studies
the 95% confidence intervals are usually used for equivalence testing.

Therefore, alpha defaults here to 0.025 (see EMEA 2000).

D. Labes

Fieller EC. *Some Problems in Interval Estimation.* J Royal Stat Soc B. 1954;16(2):175–85. doi: 10.1111/j.2517-6161.1954.tb00159.x

Sasabuchi S. *A test of a multivariate normal mean with composite hypotheses determined by linear inequalities.* Biometrika. 1980;67(2):429–39. doi: 10.1093/biomet/67.2.429

Hauschke D, Kieser M, Diletti E, Burke M. *Sample size determination for proving equivalence based on the ratio of two means for normally distributed data.* Stat Med. 1999;18(1):93–105.

Hauschke D, Steinijans V, Pigeot I. *Bioequivalence Studies in Drug Development.* Chichester: Wiley; 2007. Chapter 10.

European Agency for the Evaluation of Medicinal Products, CPMP. *Points to Consider on Switching between Superiority and Non-Inferiority.* London, 27 July 2000. CPMP/EWP/482/99

`power.RatioF`

# sample size for a 2x2 cross-over study # with CVw=0.2, CVb=0.4 # alpha=0.025 (95% CIs), target power = 80% # 'true' ratio = 95%, BE acceptance limits 80-125% # using all the defaults: sampleN.RatioF(CV = 0.2, CVb = 0.4) # gives n=28 with an achieved power of 0.807774 # see Hauschke et.al. (2007) Table 10.3a # sample size for a 2-group parallel study # with CV=0.4 (total variability) # alpha=0.025 (95% CIs), target power = 90% # 'true' ratio = 90%, BE acceptance limits 75-133.33% sampleN.RatioF(targetpower = 0.9, theta1 = 0.75, theta0 = 0.90, CV = 0.4, design = "parallel") # gives n=236 with an achieved power of 0.900685 # see Hauschke et.al. (2007) Table 10.2 # a rather strange setting of ratio0! have a look at n. # it would be better this is not the sample size but your account balance ;-). sampleN.RatioF(theta0 = 0.801, CV = 0.2, CVb = 0.4)

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