View source: R/SampleSize_doseprop.R

sampleN.dp | R Documentation |

Performes a sample size estimation for dose-proportionality studies using the power model for cossover (Latin square), parallel group designs or incomplete block designs via a confidence interval equivalence criterion.

sampleN.dp(alpha = 0.05, CV, doses, targetpower = 0.8, beta0, theta1 = 0.8, theta2 = 1/theta1, design = c("crossover", "parallel", "IBD"), dm=NULL, CVb, print = TRUE, details = FALSE, imax = 100)

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

`CV` |
Coefficient of variation. Is intra-subject CV for |

`doses` |
Vector of dose values under study. At least two doses have to be given. |

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

`beta0` |
‘True’ or assumed slope of the 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. |

`print` |
If |

`details` |
If |

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

The sample size is estimated via iterative evaluation of `power.dp()`

.

Start value for the sample size search is taken from a large sample approximation.

The sample size is bound to number of dose or sequence groups as minimum.

Balanced designs are used although this is not absolutely necessary.

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 and results will be returned.

The `Sample size`

column contains the total sample size.

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

reliable test cases are missing.

D. Labes

Chow SC, Liu JP. *Design and Analysis of Bioavailability and Bioequivalence Studies.* Boca Raton: CRC Press; 3^{rd} edition 2009.

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

`power.dp`

, `bib.CL`

# using all the defaults, i.e. crossover design, alpha=0.05 # theta1=0.8, theta2=1.25 but true slope slightly off 1 sampleN.dp(CV = 0.2, doses = c(1, 2, 8), beta0 = 1.02) # should give n=18, power=0.854528 # incomplete block design with 5 doses, 3 periods # from library(crossdes) doses <- c(5, 25, 50, 100, 200) CVb <- mse2CV(0.8) levels <- length(doses) per <- 3 block <- levels*(levels-1)/(per-1) # IBD based on balanced minimal repeated measurements design # gives n=30 and 10 sequences ibd <- crossdes::balmin.RMD(levels, block, per) sampleN.dp(CV = 0.2, doses = doses, beta0 = 1, design = "IBD", dm = ibd, CVb = CVb, targetpower=0.9)

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