CVpooled | R Documentation |

This function pools CVs of several studies.

CVpooled(CVdata, alpha = 0.2, logscale = TRUE, robust = FALSE) ## S3 method for class 'CVp' print(x, digits = 4, verbose = FALSE, ...)

`CVdata` |
A data.frame that must contain the columns |

`alpha` |
Error probability for calculating an upper confidence limit of the pooled CV. |

`logscale` |
Should the calculations be done for log-transformed data? Defaults to |

`robust` |
Defaults to |

`x` |
An object of class |

`digits` |
Number of significant digits for the |

`verbose` |
Defaults to |

`...` |
More args to print(). None used. |

The pooled CV is obtained from the weighted average of the error variances obtained from
the CVs of the single studies, weights are the degrees of freedom `df`

.

If only `n`

is given in the input `CVdata`

, the dfs are calculated via
the formulas given in `known.designs()`

. If both `n`

and `df`

are given
the `df`

column precedes.

If `logscale=TRUE`

the error variances are obtained via function `CV2se()`

.
Otherwise the pooled CV is obtained via pooling the CV^2.

A list of class `"CVp"`

with components

`CV` |
value of the pooled CV |

`df` |
pooled degrees of freedom |

`CVupper` |
upper confidence interval of the pooled CV |

`alpha` |
input value |

The class `"CVp"`

has a S3 methods `print.CVp`

.

Pooling of CVs from parallel group and crossover designs does not make any sense.

Also the function *does not* throw an error if you do so.

The calculations for `logscale=FALSE`

are not described in the references.
They are implemented by analogy to the case via log-transformed data.

The calculations are based on a common variance of Test and Reference formulations
in replicate crossover studies or a parallel group study, respectively.

D. Labes

H. Schütz’ presentations about sample size challenges.

Patterson S, Jones B. *Bioequivalence and Statistics in Clinical Pharmacology.* Boca Raton: Chapman & Hall / CRC Press; 2^{nd} edition 2017. Chapter 5.7 “Determining Trial Size”.

`known.designs, CVfromCI `

# some data: # the values for AUC, study 1 and study 2 are Example 3 of H. Schuetz' presentation CVs <- (" PKmetric | CV | n |design| source AUC | 0.20 | 24 | 2x2 | study 1 Cmax | 0.25 | 24 | 2x2 | study 1 AUC | 0.30 | 12 | 2x2 | study 2 Cmax | 0.31 | 12 | 2x2 | study 2 AUC | 0.25 | 12 | 2x2x4| study 3 (full replicate) ") txtcon <- textConnection(CVs) CVdata <- read.table(txtcon, header = TRUE, sep = "|", strip.white = TRUE, as.is = TRUE) close(txtcon) # evaluation of the AUC CVs CVsAUC <- subset(CVdata, PKmetric == "AUC") CVpooled(CVsAUC, alpha = 0.2, logscale = TRUE) # df of the 'robust' evaluation CVpooled(CVsAUC, alpha = 0.2, logscale = TRUE, robust = TRUE) # print also the upper CL, data example 3 CVsAUC3 <- subset(CVsAUC,design != "2x2x4") print(CVpooled(CVsAUC3, alpha = 0.2, robust = TRUE), digits = 3, verbose = TRUE) # will give the output: # Pooled CV = 0.235 with 32 degrees of freedom (robust dfs) # Upper 80% confidence limit of CV = 0.266 # # Combining CVs from studies evaluated by ANOVA (robust=FALSE) and # by a mixed effects model (robust=TRUE). dfs have to be provided! CVs <- (" CV | n |design| source | model | df 0.212 | 24 | 2x2 | study 1 | fixed | 22 0.157 | 27 | 3x3 | study 2 | fixed | 50 0.148 | 27 | 3x3 | study 3 | mixed | 24 ") txtcon <- textConnection(CVs) CVdata <- read.table(txtcon, header = TRUE, sep = "|", strip.white = TRUE, as.is = TRUE) close(txtcon) print(CVpooled(CVdata, alpha = 0.2), digits = 3, verbose = TRUE) # will give the output: # Pooled CV = 0.169 with 96 degrees of freedom # Upper 80% confidence limit of CV = 0.181

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