T2EQ-package {T2EQ}

Functions for Applying the T^2-Test for Equivalence

Description

Contains functions for applying the T^2-test for equivalence. The T^2-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the Mahalanobis distance. For multivariate normally distributed data the T^2-test for equivalence is exact and UMPI. The function T2EQ() implements the T^2-test for equivalence according to Wellek (2010). The function T2EQ.dissolution.profiles.hoffelder() implements a variant of the T^2-test for equivalence according to Hoffelder (2016) for the equivalence comparison of highly variable dissolution profiles.

Details

Index of help topics:

T2EQ                    Function for applying the T^2-test for
                        equivalence
T2EQ-package            Functions for Applying the T^2-Test for
                        Equivalence
T2EQ.dissolution.profiles.hoffelder
                        The T^2-test for equivalence for dissolution
                        data
ex_data_JoBS            Example dataset from Hoffelder et al. (2015)
ex_data_pharmind        Example dataset from Hoffelder (2016)

Author(s)

Thomas Hoffelder

Maintainer: Thomas Hoffelder <[e-mail]>

References

Wellek, S. (2010), Testing Statistical Hypotheses of Equivalence and Noninferiority. Second edition. Boca Raton: Chapman & Hall/CRC.

Hoffelder, T., Goessl, R., Wellek, S. (2015). Multivariate Equivalence Tests for Use in Pharmaceutical Development. Journal of Biopharmaceutical Statistics, 25:3, 417-437. URL: http://dx.doi.org/10.1080/10543406.2014.920344

Hoffelder, T. (2016). Highly Variable Dissolution Profiles: Comparison of T^2-Test for Equivalence and f_2 Based Methods. pharmind, 78:4, 587-592. URL: http://www.ecv.de/suse_item.php?suseId=Z|pi|8430

Tsong, Y., Hammerstrom, T., Sathe, P., Shah, V.P. (1996). Statistical Assessment of Mean Differences between two Dissolution Data Sets. Drug Information Journal, 30:4, 1105-1112. URL: http://dx.doi.org/10.1177/009286159603000427

EMA (2010). Guidance on the Investigation of Bioequivalence. URL: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2010/01/WC500070039.pdf

Examples

## Not run: A recalculation of the example evaluation in Hoffelder et al. (2015) 
can be done with the following code:
## End(Not run)

data(ex_data_JoBS)
REF_JoBS <- cbind(ex_data_JoBS[ which(ex_data_JoBS$Group=='REF'), ]
            [c("Diss_15_min","Diss_20_min","Diss_25_min")])
TEST_JoBS <- cbind(ex_data_JoBS[ which(ex_data_JoBS$Group=='TEST'), ]
            [c("Diss_15_min","Diss_20_min","Diss_25_min")])
equivalence_margin_JoBS <- 0.74^2
test_T2EQ_JoBS <- T2EQ(X=REF_JoBS,Y=TEST_JoBS,eq_margin = equivalence_margin_JoBS)

## Not run: A recalculation of the results underlying Figure 1 in Hoffelder (2016) 
can be done with the following code:
## End(Not run)

data(ex_data_pharmind)
REF_pharmind <- cbind(ex_data_pharmind[ which(ex_data_pharmind$Group=='REF'), ]
                  [c("Diss_10_min","Diss_20_min","Diss_30_min")])
TEST_pharmind <- cbind(ex_data_pharmind[ which(ex_data_pharmind$Group=='TEST'), ]
                  [c("Diss_10_min","Diss_20_min","Diss_30_min")])
test_T2EQ.dissolution.profiles.hoffelder_pharmind <- 
      T2EQ.dissolution.profiles.hoffelder(X=REF_pharmind,Y=TEST_pharmind)