PowerTOST [Power / Sample Size]

posted by Helmut Homepage – Vienna, Austria, 2022-05-03 14:38 (24 d 02:51 ago) – Posting: # 22955
Views: 434

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"  # 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.

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