Bioequivalence and Bioavailability Forum

BE-proff ● 2017-07-20 16:09 (2803 d 01:22 ago) Posting: # 17577 Views: 9,714	How to create table of results [Power / Sample Size] Post reply
	Hi All, I need to calculate sample sizes providing CV=0.26 and ratio varying from 0.85 to 1.20 with step 0,05. How to make a script in R which returns a table with results? Thank you Edit: Category changed. [Helmut]

ElMaestro
★★★

Denmark,
2017-07-20 16:32
(2803 d 00:59 ago)

@ BE-proff
Posting: # 17578
Views: 8,759

How to create table of results

Post reply

Hi BE-proff,

try this:

library(PowerTOST)



arrGMR=c(0.85, 0.9, 0.95, 1.0, 1.05, 1.10, 1.15, 1.20)

myCV=0.26

N=arrGMR



for (i in 1:length(arrGMR))

   N[i]=sampleN.TOST(theta0=arrGMR[i], CV=myCV, targetpower=0.8) [1,7]  



A=data.frame(arrGMR, N)



print(A)

you can easily put it inside a little function if the purpose is specifcally to "return" the data frame (as in, a table).

You can make the code totally fancier and totally unreadable by using sapply or some similar function - I am sure this is what the code police recommends. :-D

—
Pass or fail!
ElMaestro

BE-proff ● 2017-07-20 17:02 (2803 d 00:29 ago) @ ElMaestro Posting: # 17579 Views: 8,736	How to create table of results Post reply
	Hi ElMaestro, Many thanks for the code but I am just user of scripts So that recommendations like "insert" or "use function" are not for me But you are right - the purpose is to make a table with results

Helmut
★★★

Vienna, Austria,
2017-07-20 17:18
(2803 d 00:13 ago)

@ BE-proff
Posting: # 17580
Views: 8,846

How to create table of sample sizes / powers

Post reply

Hi BE-proff,

try this (for 80 and 90% target power):

CV <- 0.26 theta0 <- seq(0.85, 1.20, 0.05) thetas <- length(theta0) target <- c(0.8, 0.9) result <- data.frame(CV=rep(CV, thetas), GMR=sprintf("%.4f", theta0), n1=rep(NA, thetas), pwr1=rep(NA, thetas), n2=rep(NA, thetas), pwr2=rep(NA, thetas)) names(result)[3:6] <- rep(c("n", "power"), 2) for (j in seq_along(theta0)) { x1 <- sampleN.TOST(CV=CV, theta0=theta0[j], targetpower=target[1], print=F) x2 <- sampleN.TOST(CV=CV, theta0=theta0[j], targetpower=target[2], print=F) result[j, 3] <- x1[["Sample size"]] result[j, 4] <- sprintf("%.2f%%", 100*x1[["Achieved power"]]) result[j, 5] <- x2[["Sample size"]] result[j, 6] <- sprintf("%.2f%%", 100*x2[["Achieved power"]]) } print(result, row.names=FALSE)

Should give:

CV GMR n power n power 0.26 0.8500 222 80.09% 308 90.15% 0.26 0.9000 60 80.18% 84 90.57% 0.26 0.9500 30 80.63% 40 90.31% 0.26 1.0000 24 80.03% 30 90.12% 0.26 1.0500 30 81.51% 40 91.01% 0.26 1.1000 52 80.76% 70 90.02% 0.26 1.1500 118 80.09% 164 90.23% 0.26 1.2000 488 80.09% 674 90.01%

Note that power curves are not symmetrical around 100%. If you assume a ∆ of 10% (without knowing whether T will be lower or higher than R), work with 0.90. The sample size will cover 1.10 as well.
Try this:

theta0 <- seq(0.85, 1, 0.05) theta0 <- unique(c(theta0, rev(1/theta0))) thetas <- length(theta0)

Should give:

CV GMR n power n power 0.26 0.8500 222 80.09% 308 90.15% 0.26 0.9000 60 80.18% 84 90.57% 0.26 0.9500 30 80.63% 40 90.31% 0.26 1.0000 24 80.03% 30 90.12% 0.26 1.0526 30 80.63% 40 90.31% 0.26 1.1111 60 80.18% 84 90.57% 0.26 1.1765 222 80.09% 308 90.15%

Do you get the idea?

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Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

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

BE-proff ● 2017-07-20 18:00 (2802 d 23:32 ago) @ Helmut Posting: # 17582 Views: 8,664	How to create table of sample sizes / powers Post reply
	Hi Helmut, No, I didn't catch an idea. Do you think that absence of symmetry critical?

Helmut
★★★

Vienna, Austria,
2017-07-20 19:12
(2802 d 22:20 ago)

@ BE-proff
Posting: # 17583
Views: 9,020

Symmetrical in log-scale

Post reply

Hi BE-proff,

❝ No, I didn't catch an idea. :-(

❝ Do you think that absence of symmetry critical?

Power curves are asymmetrical around 1 in linear scale (as is the acceptance range) but symmetrical around 0 in logarithmic scale (AR: ±0.2231). Try this (CV 0.26, GMR 0.9, n 60 for target power 80%):

library(PowerTOST) CV <- 0.26 GMR <- 0.90 theta0 <- seq(1/1.3, 1.3, length.out=501) n <- sampleN.TOST(CV=0.26, theta0=GMR, print=FALSE)[["Sample size"]] power <- numeric(length(theta0)) for (j in seq_along(theta0)) { power[j] <- power.TOST(CV=CV, theta0=theta0[j], n=n) } dev.new(record=TRUE) op <- par(no.readonly=TRUE) par(pty="s", ask=TRUE) # linear scale # plot(theta0, power, type="l", las=1, col="blue", lwd=2, xlim=c(2-max(theta0), max(theta0)), ylim=c(0, 1)) grid() abline(h=0.05, col="red", lty="dotted") abline(v=c(1, GMR, 1/GMR), col=c("black", rep("blue", 2))) mtext(sprintf("%.4f", c(GMR, 1/GMR)), side=3, at=c(GMR, 1/GMR), line=1) # logarithmic scale # plot(log(theta0), power, type="l", las=1, col="blue", lwd=2, xlim=c(-1, +1)*max(abs(range(log(theta0)))), ylim=c(0, 1)) abline(h=0.05, col="red", lty="dotted") grid() abline(h=0.05, col="red", lty="dotted") abline(v=log(c(1, GMR, 1/GMR)), col=c("black", rep("blue", 2))) mtext(sprintf("%+.4f", log(c(GMR, 1/GMR))), side=3, at=log(c(GMR, 1/GMR)), line=1) par(op)

Press ⏎ or click in the graphics window to go to the next plot. To switch between plots navigate to the graphics window and use page ⇑ / page ⇓ to switch plots.

[image]

The power curve is positively skewed. For any –∆ you get the same power at 1/∆. In the example: For GMR 1.1111 as for GMR 0.90 since 1/0.90 = 1.1111. In many protocols I read “planned for a T/R-ratio of 0.90 to 1.10”. Nope. If the sample size estimation was done for 0.90 it covers anything up to 1.1111. Or the other way ’round: If it was done for 1.10 it would cover anything down to only 1/1.1 = 0.9091 (and not 0.90). Hence, the latter is stupid – unless you know (!) that the GMR will be >1. If you don’t know the sign of ∆, always plan for a GMR <1 and you will be on the safe side.

The second part of the script demonstrates that in log-scale (where the entire analysis is done) everything is symmetrical indeed:

[image]

Hope that helps.

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

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

BE-proff
●

2017-07-24 13:42
(2799 d 03:50 ago)

@ Helmut
Posting: # 17599
Views: 8,540

Symmetrical in log-scale

Post reply

Hi Helmut,

Many thanks for clarification! :clap:

Could you please tell how to add study design to :confused:

library(PowerTOST)

CV     <- 0.26

theta0 <- seq(0.85, 1.20, 0.05)

thetas <- length(theta0)

target <- c(0.8, 0.9)

result <- data.frame(CV=rep(CV, thetas), GMR=sprintf("%.4f", theta0),

                     n1=rep(NA, thetas), pwr1=rep(NA, thetas),

                     n2=rep(NA, thetas), pwr2=rep(NA, thetas))

names(result)[3:6] <- rep(c("n", "power"), 2)

for (j in seq_along(theta0)) {

  x1 <- sampleN.TOST(CV=CV, theta0=theta0[j],

                     targetpower=target[1], print=F)

  x2 <- sampleN.TOST(CV=CV, theta0=theta0[j],

                     targetpower=target[2], print=F)

  result[j, 3] <- x1[["Sample size"]]

  result[j, 4] <- sprintf("%.2f%%", 100*x1[["Achieved power"]])

  result[j, 5] <- x2[["Sample size"]]

  result[j, 6] <- sprintf("%.2f%%", 100*x2[["Achieved power"]])

}

print(result, row.names=FALSE)

Helmut
★★★

Vienna, Austria,
2017-07-24 20:18
(2798 d 21:14 ago)

@ BE-proff
Posting: # 17600
Views: 8,525

RTFM

Post reply

Hi BE-proff,

no need to copypaste my entire code.

❝ Could you please tell how to add study design to :confused:

Please do your homework and RTFM:

library(PowerTOST) help(sampleN.TOST) help(known.designs)

Online:

sampleN.TOST known.designs

Add the design-string (as provided in second column of known.designs()) as an argument to sampleN.TOST(...) in the two lines starting with x1 <- and x2 <-
Easy.

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

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

How to create table of results [Power / Sample Size]

How to create table of results

How to create table of results

How to create table of sample sizes / powers

How to create table of sample sizes / powers

Symmetrical in log-scale

Symmetrical in log-scale

RTFM