View source: R/power_doseprop.R

power.dp | R Documentation |

Calculates the power of dose-proportionality studies using the power model for crossover (Latin square) or parallel group designs via a confidence interval equivalence criterion.

power.dp(alpha = 0.05, CV, doses, n, beta0, theta1 = 0.8, theta2 = 1/theta1, design = c("crossover", "parallel", "IBD"), dm = NULL, CVb)

`alpha` |
Type 1 error. Commonly set to 0.05. |

`CV` |
Coefficient of variation for intra-subject variability if |

`doses` |
Vector of dose levels. At least two doses have to be given. |

`n` |
Number of subjects. Is total number if given as scalar, else number of subjects
in the (sequence) groups. In the latter case the length of n vector has to be
the same as length of vector doses. |

`beta0` |
‘True’ slope of power model. If missing defaults to |

`theta1` |
Lower acceptance limit for the ratio of dose normalized means (Rdmn). |

`theta2` |
Upper acceptance limit for the ratio of dose normalized means (Rdmn). |

`design` |
Crossover design (default), parallel group design or incomplete block design (IBD). |

`dm` |
'Design matrix' of the incomplete block design (IBD) if |

`CVb` |
Coefficient of variation of the between-subject variability. |

The power calculations are based on TOST for testing equivalence of the slope
of the power model with alternativ hypothesis slope = 1.

Power is calculated via non-central t-approximation only.

The calculations are based on mixed effects model (random intercept aka
random subject effect). For `design="cossover"`

or `design="parallel"`

the results coincide with all-effects-fixed model.

Value of power according to the input arguments.

This function is ‘experimental’ only since it is not thorougly tested yet.
Especially for `design="IBD"`

reliable test cases are missing.

D. Labes

Patterson S, Jones B. *Bioequivalence and Statistics in Clinical Pharmacology.* Boca Raton: Chapman & Hall/CRC: 2006. p. 239.

(contains presumably a bug)

Sethuraman VS, Leonov S, Squassante L, Mitchell TR, Hale MD. *Sample size calculation for the Power Model for dose proportionality studies.* Pharm Stat. 2007;6(1):35–41. doi: 10.1002/pst.241

Hummel J, McKendrick S, Brindley C, French R. *Exploratory assessment of dose proportionality: review of current approaches and proposal for a practical criterion.* Pharm. Stat. 2009;8(1):38–49. doi: 10.1002/pst.326

`sampleN.dp`

, `bib.CL`

# using all the defaults, i.e. latin square crossover design, alpha=0.05, # beta0=1+log(0.95)/log(rd), theta1=0.8, theta2=1.25 power.dp(CV = 0.2, doses = c(1,2,8), n = 15) # # period balanced IBD with 3 doses, 2 periods and 3 sequences, ibd <- matrix(c(1, 2, 3, 2, 3, 1), nrow = 3, ncol = 2) power.dp(CV = 0.2, doses = c(1,2,8), n = 12, design = "IBD", dm = ibd) # considerably lower than 3x3 Latin square

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