get_sim_lim: Similarity limit

In disprofas: Non-Parametric Dissolution Profile Analysis

Description

The function get_sim_lim() estimates a similarity limit in terms of the “Multivariate Statistical Distance” (MSD).

Usage

get_sim_lim(mtad, lhs)

Arguments

mtad	A numeric value specifying the “maximum tolerable average difference” (MTAD) of the profiles of two formulations at all time points (in %). The default value is 10. It determines the size of the similarity limit d_g.
lhs	A list of the estimates of Hotelling's two-sample T^2 statistic for small samples as returned by the function get_hotellings().

Details

Details about the estimation of similarity limits in terms of the “Multivariate Statistical Distance” (MSD) are explained in the corresponding section below.

Value

A vector containing the following information is returned:

DM	The Mahalanobis distance of the samples.
df1	Degrees of freedom (number of variables or time points).
df2	Degrees of freedom (number of rows - number of variables - 1).
alpha	The provided significance level.
K	Scaling factor for F to account for the distribution of the T^2 statistic.
k	Scaling factor for the squared Mahalanobis distance to obtain the T^2 statistic.
T2	Hotelling's T^2 statistic (F-distributed).
F	Observed F value.
ncp.Hoffelder	Non-centrality parameter for calculation of the F statistic (T^2 test procedure).
F.crit	Critical F value (Tsong's procedure).
F.crit.Hoffelder	Critical F value (T^2 test procedure).
p.F	The p value for the Hotelling's T^2 test statistic.
p.F.Hoffelder	The p value for the Hotelling's T^2 statistic based on the non-central F distribution.
MTAD	Specified “maximum tolerable average difference” (MTAD) of the profiles of two formulations at each individual time point (in %).
Sim.Limit	Critical Mahalanobis distance or similarity limit (Tsong's procedure).

Similarity limits in terms of MSD

For the calculation of the “Multivariate Statistical Distance” (MSD), the procedure proposed by Tsong et al. (1996) can be considered as well-accepted method that is actually recommended by the FDA. According to this method, a multivariate statistical distance, called Mahalanobis distance, is used to measure the difference between two multivariate means. This distance measure is calculated as

D_M = sqrt((x_T - x_R)^{\top} S_{pooled}^{-1} (x_T - x_R)) ,

where S_{pooled} = (S_T + S_R) / 2 is the sample variance-covariance matrix pooled across the batches, x_T and x_R are the vectors of the sample means for the test (T) and reference (R) profiles, and S_T and x_R are the variance-covariance matrices of the test and reference profiles.

In order to determine the similarity limits in terms of the MSD, i.e. the Mahalanobis distance between the two multivariate means of the dissolution profiles of the formulations to be compared, Tsong et al. (1996) proposed using the equation

D_M^{max} = sqrt(d_g^{\top} S_{pooled}^{-1} d_g) ,

where d_g is a 1 x p vector with all p elements equal to an empirically defined limit d_g, e.g., 15%, for the maximum tolerable difference at all time points, and p is the number of sampling points. By assuming that the data follow a multivariate normal distribution, the 90% confidence region (CR) bounds for the true difference between the mean vectors, μ_T - μ_R, can be computed for the resultant vector μ to satisfy the following condition:

CR = sqrt((μ - (x_T - x_R))^{\top} S_{pooled}^{-1} (μ - (x_T - x_R))) ≤q F_{p, n_T + n_R - p - 1, 0.9} ,

where K is the scaling factor that is calculated as

(n_T n_R) / (n_T + n_R) * (n_T + n_R - p - 1) / ((n_T + n_R - 2) p) ,

and F_{p, n_T + n_R - p - 1, 0.9} is the 90^{th} percentile of the F distribution with degrees of freedom p and n_T + n_R - p - 1. It is obvious that (n_T + n_R) must be greater than (p + 1). The formula for CR gives a p-variate 90% confidence region for the possible true differences.

T2 test for equivalence

Based on the distance measure for profile comparison that was suggested by Tsong et al. (1996), i.e. the Mahalanobis distance, Hoffelder (2016) proposed a statistical equivalence procedure for that distance, the so-called T^2 test for equivalence (T2EQ). It is used to demonstrate that the Mahalanobis distance between reference and test group dissolution profiles is smaller than the “Equivalence Margin” (EM). Decision in favour of equivalence is taken if the p value of this test statistic is smaller than the pre-specified significance level α, i.e. if p < α. The p value is calculated by aid of the formula

p = F_{p, n_T + n_R - p - 1, ncp, α} (n_T + n_R - p - 1) / ((n_T + n_R - 2) p) T^2 ,

where α is the significance level and ncp is the so-called “non-centrality parameter” that is calculated by

(n_T n_R) / (n_T + n_R) (D_M^{max})^2 .

The test statistic being used is Hotelling's T^2 that is given as

(n_T n_R) / (n_T + n_R) * (x_T - x_R)^{\top} S_{pooled}^{-1} (x_T - x_R) .

As mentioned elsewhere, d_g is a 1 x p vector with all p elements equal to an empirically defined limit d_g. Thus, the components of the vector d_g can be interpreted as upper bound for a kind of “average” allowed difference between test and reference profiles, the “global similarity limit”. Since the EMA requires that “similarity acceptance limits should be pre-defined and justified and not be greater than a 10% difference”, it is recommended to use 10%, not 15% as proposed by Tsong et al. (1996), for the maximum tolerable difference at all time points.

References

Tsong, Y., Hammerstrom, T., Sathe, P.M., and Shah, V.P. Statistical assessment of mean differences between two dissolution data sets. Drug Inf J. 1996; 30: 1105-1112.
doi: 10.1177/009286159603000427

Wellek S. (2010) Testing statistical hypotheses of equivalence and noninferiority (2nd ed.). Chapman & Hall/CRC, Boca Raton.
doi: 10.1201/EBK1439808184

Hoffelder, T. Highly variable dissolution profiles. Comparison of T^2-test for equivalence and f_2 based methods. Pharm Ind. 2016; 78(4): 587-592.
https://www.ecv.de/suse_item.php?suseId=Z|pi|8430

See Also

mimcr, get_hotellings.

Examples

# Dissolution data of one reference batch and one test batch of n = 6
# tablets each:
str(dip1)

# 'data.frame':	12 obs. of  10 variables:
# $ type  : Factor w/ 2 levels "R","T": 1 1 1 1 1 1 2 2 2 2 ...
# $ tablet: Factor w/ 6 levels "1","2","3","4",..: 1 2 3 4 5 6 1 2 3 4 ...
# $ t.5   : num  42.1 44.2 45.6 48.5 50.5 ...
# $ t.10  : num  59.9 60.2 55.8 60.4 61.8 ...
# $ t.15  : num  65.6 67.2 65.6 66.5 69.1 ...
# $ t.20  : num  71.8 70.8 70.5 73.1 72.8 ...
# $ t.30  : num  77.8 76.1 76.9 78.5 79 ...
# $ t.60  : num  85.7 83.3 83.9 85 86.9 ...
# $ t.90  : num  93.1 88 86.8 88 89.7 ...
# $ t.120 : num  94.2 89.6 90.1 93.4 90.8 ...

# Estimation of the parameters for Hotelling's two-sample T2 statistic
# (for small samples)
hs <- get_hotellings(m1 = as.matrix(dip1[dip1$type == "R", c("t.15", "t.90")]),
                    m2 = as.matrix(dip1[dip1$type == "T", c("t.15", "t.90")]),
                    signif = 0.1)

# Estimation of the similarity limit in terms of the "Multivariate Statistical
# Distance" (MSD)for a "maximum tolerable average difference" (mtad) of 10
res <- get_sim_lim(mtad = 15, hs)

# Expected results in res
#            DM              df1              df2            alpha
#  1.044045e+01     2.000000e+00     9.000000e+00     1.000000e-01
#             K                k               T2                F
#  1.350000e+00     3.000000e+00     3.270089e+02     1.471540e+02
# ncp.Hoffelder           F.crit F.crit.Hoffelder              p.F
#  2.782556e+02     3.006452e+00     8.357064e+01     1.335407e-07
# p.F.Hoffelder             MTAD        Sim.Limit
#  4.822832e-01     1.500000e+01     9.630777e+00

disprofas documentation built on Dec. 8, 2021, 5:10 p.m.