Software? Ethics beyond statistics… [Two-Stage / GS Designs]
❝ We have worked with CI of 94.12 and alpha 0.0294
Which software did you use? The phrase
Power at 20%
Now for the nasty part. I recalculated your CVs and PEs from the CIs in order to increase precision. Your data make clear that Cmax is the crucial point (higher CV, worse PE).
library(PowerTOST)
n <- 36
lo <- 0.70162
hi <- 0.94529
CV <- signif(CI2CV(lower=lo, upper=hi, n=n, design="2x2x2"), 5)
PE <- sqrt(lo*hi)
sampleN.TOST(alpha=0.0294, CV=CV, theta0=0.95, targetpower=0.8, design="2x2x2")
+++++++++++ Equivalence test - TOST +++++++++++
Sample size estimation
-----------------------------------------------
Study design: 2x2 crossover
log-transformed data (multiplicative model)
alpha = 0.0294, target power = 0.8
BE margins = 0.8 ... 1.25
Null (true) ratio = 0.95, CV = 0.38744
Sample size (total)
n power
74 0.802127
power.TOST(alpha=0.0294, CV=CV, n=74, theta0=PE, design="2x2x2")
[1] 0.05443928
sampleN.TOST(alpha=0.0294, CV=CV, theta0=PE, targetpower=0.8, design="2x2x2")
+++++++++++ Equivalence test - TOST +++++++++++
Sample size estimation
-----------------------------------------------
Study design: 2x2 crossover
log-transformed data (multiplicative model)
alpha = 0.0294, target power = 0.8
BE margins = 0.8 ... 1.25
Null (true) ratio = 0.814392, CV = 0.38744
Sample size (total)
n power
6566 0.800031
In the future consider to add a futility criterion for early stopping and don’t perform TSDs if you are unsure about the GMR. Sorry, but I have to quote myself1
The entire arsenal of obtaining a reliable ‘educated guess’ (e.g. dissolution similarity for immediate release formulations of biopharmaceutics classification system class I/III drugs, established in vivo-in vitro correlation for controlled release products) should be used as well. If no reliable estimate can be derived, a—sufficiently large—pilot study should be performed. Subsequently, a TSD would still support dealing with the uncertain CV.
Be aware that futility rules deteriorate power. This was shown by Fuglsang2 for an upper total sample size, but is valid for any other rule as well. It would make sense to perform own simulations to get an idea about the impact of GMRs substantially deviating from 1.- References:
- Schütz H. Two-stage designs in bioequivalence trials. Eur J Clin Pharmacol. 2015;71(3):271-81. doi:10.1007/s00228-015-1806-2
- Fuglsang A. Futility rules in bioequivalence trials with sequential designs. AAPS J. 2014;16(1):79–82. doi:10.1208/s12248-013-9540-0
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Helmut Schütz
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Science Quotes
Complete thread:
- Sequential trial with B method of Potvin john john 2015-10-01 17:44 [Two-Stage / GS Designs]
- Sequential trial with B method of Potvin ElMaestro 2015-10-01 17:58
- Sequential trial with B method of Potvin Helmut 2015-10-01 18:13
- Sequential trial with B method of Potvin john john 2015-10-07 11:26
- Power to the people d_labes 2015-10-07 13:49
- Power to the people john john 2015-10-07 14:54
- Software? Ethics beyond statistics…Helmut 2015-10-07 16:06
- Subtraction of a df ElMaestro 2015-10-07 21:22
- df = df-1 d_labes 2015-10-08 08:10
- Subtraction of a df ElMaestro 2015-10-07 21:22
- Software? Ethics beyond statistics…Helmut 2015-10-07 16:06
- Power to the people john john 2015-10-07 14:54
- Potvin B: α 0.0294 in both stages Helmut 2015-10-08 15:27
- Potvin B: α 0.0294 in both stages john john 2015-10-23 12:04
- Power to the people d_labes 2015-10-07 13:49
- Sequential trial with B method of Potvin john john 2015-10-07 11:26