This function performs the power calculation of the BE decision via the FDA method for narrow therapeutic index drugs (NTID's) by simulations. The study design could be the full replicate design 2x2x4 with 4-periods or the 2x2x3 replicate design with sequences TRT|RTR.
power.NTIDFDA(alpha = 0.05, theta1, theta2, theta0, CV, n, design=c("2x2x4", "2x2x3"),
nsims = 1e+05, details = FALSE, setseed = TRUE)Type I error probability, significance level. Conventionally mostly set to 0.05.
Conventional lower ABE limit to be applied in the FDA procedure. Defaults to 0.8 if not given explicitly.
Conventional upper ABE limit to be applied in the FDA procedure. Defaults to 1.25 if not given explicitly.
'True' or assumed bioequivalence ratio. Attention! Defaults here to 0.975 if not given explicitly. The value was chosen nearer to 1 because the potency (contents) settings for NTID's are tightened by the FDA.
Coefficient(s) of variation as ratio.
If length(CV) = 1 the same CV is assumed for Test and Reference.
If length(CV) = 2 the CV for Test must be given in CV[1] and for
Reference in CV[2].
Number of subjects under study. May be given as vector. In that case it is assumed that n contains the number of subjects per sequence groups. Attention! In case of the 2x2x3 (TRT|RTR) design the order of n's important if given as vector. n[1] is for sequence group 'TRT' and n[2] is for sequence group 'RTR'. If n is given as single number (total sample size) and this number is not divisible by the number of sequences of the design an unbalanced design is assumed. A corresponding message is thrown showing the numbers of subjects in the sequence groups.
Design of the study to be planned.
2x2x4 is the full replicate design with 2 sequences and 4 periods.
2x2x3 is the 3-period replicate design with sequences TRT|RTR.
Defaults to design="2x2x4".
Number of simulations to be performed to obtain the empirical power. Defaults to 100 000 = 1e+5.
If set to TRUE the computational time is shown as well as the components
for the BE decision.
p(BE-ABE) is the simulated probability for the conventional ABE test.
p(BE-sABEc) is the probability that the 95% CI of the ABE criterion is <0.
p(BE-sratio) is the probability that the ratio of sWT/sWR is <= 2.5.
Simulations are dependent on the starting point of the (pseudo) random number
generator. To avoid differences in power for different runs a set.seed(123456)
is issued if setseed=TRUE, the default.
Returns the value of the (empirical) power if argument details=FALSE.
Returns a named vector if argument details=TRUE.
p(BE) is the power, p(BE-sABEc) is the power of the BE test via scaled ABE criterion alone,
p(BE-ABE) is the power of the conventional ABE test alone and p(BE-sratio)
is the power of the criterion 'ratio of sWT/sWR is <= 2.5' alone.
The linearized scaled ABE criterion is calculated according to the SAS code given in the FDA Warfarin guidance. For deciding BE the study must pass that criterion, the conventional ABE test and additional the test that the ratio of sWT/sWR is <= 2.5. The simulations are done via the distributional properties of the statistical quantities necessary for deciding BE based on these method. Details can be found in a document "Implementation_scaledABE_sims" located in the doc subdirectory of the package.
FDA Draft Guidance on Warfarin Sodium Recommended Dec 2012. download
Yu LX et al. Novel bioequivalence approach for narrow therapeutic index drugs Clin Pharmacol Ther. 2015;97(3):286--91. 10.1002/cpt.28
Jiang W et al. A Bioequivalence Approach for Generic Narrow Therapeutic Index Drugs: Evaluation of the Reference-Scaled Approach and Variability Comparison Criterion AAPS J. 2015;17(4):891--901. 10.1208/s12248-015-9753-5
Endr<U+00E9>nyi L, T<U+00F3>thfalusi L. Determination of Bioequivalence for Drugs with Narrow Therapeutic Index: Reduction of the Regulatory Burden J Pharm Pharm Sci. 2013;16(5):676--82. free download
sampleN.NTIDFDA
and power.HVNTID, sampleN.HVNTID for NTIDs with
high variability
# NOT RUN {
# using the all defaults:
# GMR=0.975, theta1=0.8, theta2=1.25, 100 000 sims
# and a CV of 0.1 (= 10%) with 12 subjects, balanced
power.NTIDFDA(CV=0.1, n=12)
# should give a power of 0.62553
# }
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