Within-subject variability: Property of the drug (& formulation) [General Statistics]
❝ I think intra-subject CV is an inherent characteristic of a drug. is it right?
Only if you are talking about an intravenous dose. Otherwise, it is mixture of the properties of the drug (influenced by transporters, pre-systemic and first-pass metabolism, clearance) and the formulation / physiology (absorption, lag-time due to gastric emptying). See my nitpicking paragraph in this post.
❝ If it not, does anyone can reduce it by any ways?
I can’t imagine how.1,2 In a crossover design every subject acts as its own control. Imagine you have a very high between-subject variability (e.g., extensive and poor metabolizers or one is stupid enough to include only ballerinas and sumo-wrestlers in a study). The within-subject variability will not be affected.
Try it: Use the data of any study and multiply results of half of the subjects by two (mimicking the ballerinas with low volumes of distribution) and divide the other half by two (mimicking the sumo-wrestlers). What do you get?
In a simple crossover CVintra is pooled from CVwT and CVwR (i.e., one common variance is assumed in the model). If CVwT < CVwR (the reference is a lousy product), the CI is inflated and we are punished by a higher sample size. All too bad that we don’t know the CVs in a simple 2×2 crossover. We need one the full replicate designs (see also this post) and reference-scaling for a potential incentive in the sample size.
An example: CVwT and CVwR 0.3–0.5, theta0 0.95, targetpower 0.8.
sampleN.TOST()
does not support different CVs. Makes sense, since only CVw is part of the model. We can misuse the function sampleN.scABEL()
after ‘switching off’ all scaling conditions. Does it help? Not at all. Only ABEL can be nice.library(PowerTOST)
CV.lo <- 0.30
CV.mid <- 0.40
CV.hi <- 0.50
CVwR <- CVwT <- seq(CV.lo, CV.hi, 0.1)
res <- data.frame(CVwT=rep(CVwT, each=length(CVwR)),
CVwR=rep(CVwR, length(CVwT)), CVw=NA,
n.ABE=NA, n.ABE.sim=NA, n.ABEL=NA)
res$CVw <- mse2CV((CV2mse(res$CVwT)+CV2mse(res$CVwR))/2)
# remove all scaling conditions
reg <- reg_const("USER", r_const=log(1.25)/CV2se(0.3),
CVswitch=0.3, CVcap=0.3, pe_constr=FALSE)
for (j in 1:nrow(res)) {
res$n.ABE[j] <- sampleN.TOST(CV=res$CVw[j], theta0=0.95, design="2x2x4",
print=FALSE, details=FALSE)[["Sample size"]]
res$n.ABE.sim[j] <- sampleN.scABEL(CV=c(res$CVwT[j], res$CVwR[j]),
theta0=0.95, design="2x2x4",
regulator=reg, print=FALSE,
details=FALSE)[["Sample size"]]
res$n.ABEL[j] <- sampleN.scABEL(CV=c(res$CVwT[j], res$CVwR[j]),
theta0=0.95, design="2x2x4",
regulator="EMA", print=FALSE,
details=FALSE)[["Sample size"]]
}
print(res[with(res, order(CVw, CVwT)), ], row.names=FALSE)
CVwT CVwR CVw n.ABE n.ABE.sim n.ABEL
0.3 0.3 0.3000000 20 20 18
0.3 0.4 0.3527824 26 26 16
0.4 0.3 0.3527824 26 26 24
0.4 0.4 0.4000000 34 34 20
0.3 0.5 0.4089765 34 34 16
0.5 0.3 0.4089765 34 34 32
0.4 0.5 0.4518401 42 42 18
0.5 0.4 0.4518401 42 42 24
0.5 0.5 0.5000000 50 50 22
- I once heard about a Russian CRO claiming that “our volunteers have a lower CVintra and studies at our site can be smaller than usual”. What the heck are they doing? Include monozycotic twins or have access to human clones? Would violate the assumption of independence in the model. I guess, they are just fabricating data.
- Some people claim that extreme standardization (BMI, …) will also help to reduce CVintra. I strongly doubt that since the between- and within-variance components are mutually independent.
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
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Helmut Schütz
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The quality of responses received is directly proportional to the quality of the question asked. 🚮
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Complete thread:
- how to calculate MSEw and MSEb verstc 2018-06-12 02:23 [General Statistics]
- Get software! Helmut 2018-06-12 16:54
- Get software! verstc 2018-06-17 14:55
- Within-subject variability: Property of the drug (& formulation)Helmut 2018-06-17 16:10
- Within-subject variability: Property of the drug (& formulation) nobody 2018-06-19 10:20
- Within-subject variability: Property of the drug (& formulation)Helmut 2018-06-17 16:10
- Get software! verstc 2018-06-17 14:55
- Get software! Helmut 2018-06-12 16:54