TRR.RTR.RRT: Reference Datasets for TRR|RTR|RRT (partial) Replicate...

TRR.RTR.RRTR Documentation

Reference Datasets for TRR|RTR|RRT (partial) Replicate Designs

Description

Datasets from the public domain or simulated to be evaluated by method.A(), method.B(), or ABE().

Format

  • Reference Dataset 02
    24 subjects.
    Balanced (eight subjects in each of the three sequences) and complete (no missing data). No outliers.
    A data frame with 72 observations on the following 6 variables:

    rds02
    subject a factor with 24 levels: 1, 2, ..., 24
    period a factor with 3 levels: 1, 2, 3
    sequence a factor with 3 levels: TRR, RTR, RRT
    treatment a factor with 2 levels: T, R
    PK a numeric vector of pharmacokinetic responses acceptable for reference-scaling (generally Cmax)
    logPK a numeric vector of the natural logarithms of PK

    In the source evaluated by SAS v9.1 for ABEL. Reported results:

    SAS Proc GLM
    CVwR 11.2%
    PE 102.26% (Method A and B)
    90% CI 97.32% – 107.46% (Method A and B)
  • Reference Dataset 04
    Data set of Table II given by Patterson & Jones. 51 subjects.
    Balanced (17 subjects in each of the three sequences) and complete. No outliers.
    A data frame with 153 observations on the following 5 variables:

    rds04
    subject a factor with 51 levels: 1, 2, ..., 56
    period a factor with 3 levels: 1, 2, 3
    sequence a factor with 3 levels: TRR, RTR, RRT
    treatment a factor with 2 levels: T, R
    PK a numeric vector of pharmacokinetic responses (here Cmax)

    In the source evaluated by SAS with the FDA’s mixed effects model (termed ‘Method C’ by the EMA; not compatible with the guideline). Reported results:

    SAS Proc MIXED
    CVwR 61%
    PE 137%
    90% CI 119% – 159%
  • Reference Dataset 07
    Simulated with CVwT = CVwR = 35%, GMR 0.90. 360 subjects.
    Balanced (120 subjects in each of the three sequences) and complete. No outliers.
    A data frame with 1,080 observations on the following 5 variables:

    rds07
    subject a factor with 360 levels: 1, 2, ..., 360
    period a factor with 3 levels: 1, 2, 3
    sequence a factor with 3 levels: TRR, RTR, RRT
    treatment a factor with 2 levels: T, R
    PK a numeric vector of pharmacokinetic responses (generally Cmax)
  • Reference Dataset 30
    Simulated with heteroscedasticity (CVwT = 14%, CVwR = 28%, CVbT = 28%, CVbR = 56%), GMR = 0.90. 12 subjects. 14 subjects.
    Imbalanced (six subjects in sequence TRR, five in RTR, and three RRT) and incomplete (two missings in sequences TRR and RTR and three in sequence RRT). Missings / period: 0/1, 0/2, 7/3. No outliers.
    A data frame with 35 observations on the following 5 variables:

    rds30
    subject a factor with 14 levels: 1, 2, ..., 39
    period a factor with 3 levels: 1, 2, 3
    sequence a factor with 3 levels: TRR, RTR, RRT
    treatment a factor with 2 levels: T, R
    PK a numeric vector of pharmacokinetic responses (generally Cmax)

Details

Dataset N CVwR (%) Evaluation
rds02 24 <30 method.A(), method.B(), ABE()
rds04 51 >30 method.A(), method.B()
rds07 360 >30 method.A(), method.B()
rds30 14 <30 method.A(), method.B(), ABE()

Note

In software sequences and treatments are ranked in lexical order. Hence, executing str() or summary() will show sequence as "RRT", "RTR", "TRR" and treatment as "R", "T". In BE – by convention – sequences are ordered with T first. The package follows this convention.

Author(s)

Helmut Schütz (R-code for simulations by Detlew Labes)

Source

Dataset Origin Description
rds02 EMA Annex III.
rds04 Patterson & Jones Cmax data of Table II.
rds07 R Large simulated data set with homoscedasticity.
rds30 R Simulated with heteroscedasticity; imbalanced and incomplete.

References

European Medicines Agency. London, 21 September 2016. Annex I, Annex III.

Patterson SD, Jones B. Viewpoint: observations on scaled average bioequivalence. Pharm Stat. 2012; 11(1): 1–7. doi: 10.1002/pst.498

Examples

str(rds02)
row <- c(10:12, 1:3, 16:18)
rds02[row, ]
summary(rds02[2:6])

replicateBE documentation built on May 3, 2022, 1:06 a.m.