PowerTOST [Power / Sample Size]

posted by Helmut Homepage – Vienna, Austria, 2022-05-03 14:38 (147 d 08:08 ago) – Posting: # 22955
Views: 816

Hi pharm07,

as suggested by Dshah, assuming a T/R-ratio of 0.975 and a CV of 0.1:

library(PowerTOST) # attach the library
theta0 <- 0.975    # assumed T/R-ratio
CV     <- 0.10     # intra-subject CV (assuming CVwT = CVwR)
target <- 0.80     # target power
design <- "2x2x4"  # 4-period full replicate mandatory for the FDA

# EMA and most others:
sampleN.TOST(CV = CV, theta0 = theta0, theta1 = 0.90,
             design = design, targetpower = target)

+++++++++++ Equivalence test - TOST +++++++++++
            Sample size estimation
-----------------------------------------------
Study design: 2x2x4 (4 period full replicate)
log-transformed data (multiplicative model)

alpha = 0.05, target power = 0.8
BE margins = 0.9 ... 1.111111
True ratio = 0.975,  CV = 0.1

Sample size (total)
 n     power
12   0.856278


# FDA and China CDE:
sampleN.NTID(CV = CV, theta0 = theta0, design = design,
             targetpower = target)

+++++++++++ FDA method for NTIDs ++++++++++++
           Sample size estimation
---------------------------------------------
Study design:  2x2x4 (TRTR|RTRT)
log-transformed data (multiplicative model)
1e+05 studies for each step simulated.

alpha  = 0.05, target power = 0.8
CVw(T) = 0.1, CVw(R) = 0.1
True ratio     = 0.975
ABE limits     = 0.8 ... 1.25
Implied scABEL = 0.9002 ... 1.1108
Regulatory settings: FDA
- Regulatory const. = 1.053605
- 'CVcap'           = 0.2142

Sample size search
 n     power
14   0.717480
16   0.788690
18   0.841790


# Beware of unequival variances!
CV.bad  <- signif(CVp2CV(CV, ratio = 1.5), 4)     # T worse than R
sampleN.NTID(CV = CV.bad, theta0 = theta0, design = design,
             targetpower = target)

+++++++++++ FDA method for NTIDs ++++++++++++
           Sample size estimation
---------------------------------------------
Study design:  2x2x4 (TRTR|RTRT)
log-transformed data (multiplicative model)
1e+05 studies for each step simulated.

alpha  = 0.05, target power = 0.8
CVw(T) = 0.1096, CVw(R) = 0.0894
True ratio     = 0.975
ABE limits     = 0.8 ... 1.25
Implied scABEL = 0.9103 ... 1.0986
Regulatory settings: FDA
- Regulatory const. = 1.053605
- 'CVcap'           = 0.2142

Sample size search
 n     power
20   0.758770
22   0.805070


CV.good <- signif(CVp2CV(CV, ratio = 1 / 1.5), 4) # T better than R
sampleN.NTID(CV = CV.good, theta0 = theta0, design = design,
             targetpower = target)

+++++++++++ FDA method for NTIDs ++++++++++++
           Sample size estimation
---------------------------------------------
Study design:  2x2x4 (TRTR|RTRT)
log-transformed data (multiplicative model)
1e+05 studies for each step simulated.

alpha  = 0.05, target power = 0.8
CVw(T) = 0.0894, CVw(R) = 0.1096
True ratio     = 0.975
ABE limits     = 0.8 ... 1.25
Implied scABEL = 0.8912 ... 1.1220
Regulatory settings: FDA
- Regulatory const. = 1.053605
- 'CVcap'           = 0.2142

Sample size search
 n     power
12   0.735990
14   0.814770


More details and examples in this article.

Dif-tor heh smusma 🖖 Довге життя Україна! [image]
Helmut Schütz
[image]

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes

Complete thread:

UA Flag
Activity
 Admin contact
22,391 posts in 4,685 threads, 1,595 registered users;
online 14 (0 registered, 14 guests [including 4 identified bots]).
Forum time: Tuesday 22:47 CEST (Europe/Vienna)

Statistics. A sort of elementary form of mathematics which consists of
adding things together and occasionally squaring them.    Stephen Senn

The Bioequivalence and Bioavailability Forum is hosted by
BEBAC Ing. Helmut Schütz
HTML5