Bioequivalence and Bioavailability Forum

Alyssa
☆

Malaysia,
2020-05-04 06:35
(1682 d 07:50 ago)

Posting: # 21389
Views: 13,488

Sample size calculation (Pilot study result vs literature) [Power / Sample Size]

Hi,

We have a product, conducted pilot study, 2x2x2 , 18 subjects.

Geometric Lease square Mean Cmax (ng/mL) T = 15032.282
Geometric Lease square Mean Cmax (ng/mL) R = 15227.106
T/R ratio % = 98.70
90% CI = 84.41 – 115.46
ISCV (%) = 26.4
Power(%) = 76.6%

However, based on published literature (Public assessment report, PAR) for the same product. 3 way crossover, reference replicate with 41 subjects completed, the results shows below:-

Geometric Lease square Mean Cmax (ng/mL) T = 16530.654
Geometric Lease square Mean Cmax (ng/mL) R = 15064.108
T/R ratio % = 109.7
90% CI = 96.25-125.11%
ISCV (%) = 42.6%
Power(%) = NA

Based on the CRO in house data for their past experience, Range of Intra-Subject Variability of Reference Formulation (CVWR): 26.8% – 50.3% (Pass studies)

Question:
1. Usually ISCV result of the 3 way crossover or 4 way crossover, replicate study published in the PAR is CVwR or CVw?

2. Which ISCV should i consider to use for sample size calculation as our own pilot study shows promising result, however, published literature and CRO past experience shows the other way. CRO advised us to go for 4 way crossover, replicate design, with ratio 90-111%, ISCV = 35-40%

3. What would be the recommended ISCV and study design for product like this? I have to balance between passing the study and the company's budget.

Hope the expert here can give me some recommendation. Appreciated. TQ :-)

Dr_Dan
★★

Germany,
2020-05-04 10:46
(1682 d 03:39 ago)

@ Alyssa
Posting: # 21391
Views: 11,350

Sample size calculation (Pilot study result vs literature)

Post reply

Dear Alyssa
I guess you are talking about an oral IR formulation, right?
ISCV are not carved in stone, there will always be a certain variability. However, big differences hint on differences in study performance and/or quality of formulations. It is reasonable to assume that the ISCV in the pivotal study will be similar to the pilot study if you use the same CRO (study procedure performance), the same test formulation vs. the same (batch of) reference product and the same bioanalytical method. If costs do not matter you can of course follow the suggestion of the CRO to go for 4 way crossover, replicate design, with ratio 90-111%, ISCV = 35-40%. :-D

I hope this helps

—
Kind regards and have a nice day
Dr_Dan

Alyssa
☆

Malaysia,
2020-05-04 11:26
(1682 d 02:59 ago)

@ Dr_Dan
Posting: # 21392
Views: 11,260

Sample size calculation (Pilot study result vs literature)

Post reply

Dear Dr. Dan,

Thanks for your reply.

❝ I guess you are talking about an oral IR formulation, right?

Is a oral MR formulation.

❝ It is reasonable to assume that the ISCV in the pivotal study will be similar to the pilot study if you use the same CRO (study procedure performance), the same test formulation vs. the same (batch of) reference product and the same bioanalytical method.

The CRO we used for pilot study is the same as per the one used in the published literature (sponsor mentioned the CRO name in the PAR). Therefore, the study procedure performance and bioanalytical method variability is reduced.

❝ If costs do not matter you can of course follow the suggestion of the CRO to go for 4 way crossover, replicate design, with ratio 90-111%, ISCV = 35-40%. :-D :-D :-D

Cost is definitely a matter for us now, especially during this Covid-19 pandemic, have to spend wisely. Therefore i need some expert opinion here for my consideration. :confused:

Edit: Full quote removed. Please delete everything from the text of the original poster which is not necessary in understanding your answer; see also this post #5! [Helmut]

Helmut
★★★

Vienna, Austria,
2020-05-04 14:07
(1682 d 00:18 ago)

@ Alyssa
Posting: # 21393
Views: 11,196

Estimation of CVw and/or CVwR

Post reply

Hi Alyssa,

❝ Usually ISCV result of the 3 way crossover or 4 way crossover, replicate study published in the PAR is CVwR or CVw?

There are no rules (it depends on what the assessor decides to include of the study report). However, since the study was a replicate design with reference-scaling, possibly it is CV_wR (more important).
We can estimate the CV_w with the R-package PowerTOST:

library(PowerTOST) round(100*CVfromCI(lower = 0.9625, upper = 1.2511, n = 41, design = "3x6x3"), 1)

and get

Unbalanced 3x6x3 design. n(i)= 7/7/7/7/7/6 assumed. [1] 36.8

~~Since this does not match what is given in the PAR, it is a strong hint that the 42.6% is the CV_wR~~.
[Nonsense, not a Williams’ design! See ElMaestro’s post and the correction.]
If the expanded limits are given in the PAR, you can estimate the CV_wR from the upper limit by the function CVwRfromU() to check. Example for 136.4%:

round(100 * CVwRfromU(136.4 / 100), 1) [1] 42.6

Although CV_wT is not accessible in a partial replicate design, CV_w < CV_wR means that the test is less variable than the reference (since CV_w is pooled from CV_wR and CV_wT).

I agree with Dan but want to add one point. In my experience the variability across studies (with the same clinical setup, bioanalytical method, ) tends to be more “stable” than the T/R-ratio. Hence, don’t fall into the trap of believing the nice 98.7% you observed in the pilot study. It might well have been pure chance. For HVD(P)s assuming a T/R-ratio of better than 90–111% is not a good idea (recommended by the two Lászlós* and therefore, the default in functions sampleN.scABEL() and sampleN.RSABE() of PowerTOST).

PS: Avoid sample size “calculation” if you don’t mind. Use “estimation” instead. ;-)

PPS: The partial replicate is a lousy design. If you want to have only three periods I suggest the 2×2×3 full replicate TRT|RTR instead. If you insist in the partial replicate (why?), use the function sampleN.scABEL.sdsims(). Slower than sampleN.scABEL() but more accurate. For a comparison see the vignette and scroll down to “Heterogenicity”.

Tóthfalusi L, Endrényi L. Sample Sizes for Designing Bioequivalence Studies for Highly Variable Drugs. J Pharm Pharmaceut Sci. 2011;15(1):73–84. open access.

—
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

ElMaestro ★★★ Denmark, 2020-05-04 14:14 (1682 d 00:11 ago) @ Helmut Posting: # 21394 Views: 11,233	Estimation of CVw and/or CVwR Post reply
	Hi Hötzi, the powerTOST package is excellent and I am not myself a very experienced user (yet) as I tend to rely on much less reliable code of my own making So forgive me I am asking: I think the OP implied a partial replicate. TRR/RRT/RTR is not 3x6x3 but rather 2(trts)x3(seqs)x3(pers), is there a reason to plug in 3x6x3 in your code? — Pass or fail! ElMaestro

Helmut
★★★

Vienna, Austria,
2020-05-04 14:40
(1681 d 23:46 ago)

@ ElMaestro
Posting: # 21395
Views: 11,405

Mea culpa!

Post reply

Hi ElMaestro,

❝ the powerTOST package is excellent …

THX!

❝ … and I am not myself a very experienced user (yet) as I tend to rely on much less reliable code of my own making :-D

Sure. In line what Jaime once stated:

Never trust in any piece of software you haven't written yourself
(and even then you should be cautious…)

❝ So forgive me I am asking: I think the OP implied a partial replicate. TRR/RRT/RTR is not 3x6x3 but rather 2(trts)x3(seqs)x3(pers), …

Oops! You are absolutely right.

❝ ❝ 3 way crossover, reference replicate

❝ … is there a reason to plug in 3x6x3 in your code?

I screwed up! Of course, not a 3-period Williams’ design. Hence,

library(PowerTOST) round(100 * CVfromCI(lower = 0.9625, upper = 1.2511, n = 41, design = "2x3x3"), 2)

and

Unbalanced 2x3x3 design. n(i)= 14/14/13 assumed. [1] 42.97

That’s reasonably close to the 42.6% given in the PAR. Sorry for the confusion caused!

@Alyssa: Can you please check whether the expanded limits are also given in the PAR? Based on CV_wR 42.6% they should be close to

round(100 * scABEL(CV = 42.6 / 100), 1) lower upper 73.3 136.4

—
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

ElMaestro ★★★ Denmark, 2020-05-04 15:46 (1681 d 22:40 ago) @ Helmut Posting: # 21396 Views: 11,250	It must be Myco Post reply
	Hi again, I believe it must be this assessment report.: "the acceptance criteria for Cmax was widened to the acceptance range of 72.83-137.31%", — Pass or fail! ElMaestro

Helmut
★★★

Vienna, Austria,
2020-05-04 16:07
(1681 d 22:19 ago)

@ ElMaestro
Posting: # 21397
Views: 11,147

It is, it is!

Post reply

❝ I believe it must be this assessment report.:

❝ "the acceptance criteria for Cmax was widened to the acceptance range of 72.83-137.31%",

Almost my dear Dr Watson!
It’s the first study; results on page 7:
“… within-reference intra-subject CV of ln-transformed C_max > 30% (42.6%), hence C_max limits were widen[ed] to 73.31–136.42% using scaled-average-bioequivalence.”

library(PowerTOST) identical(42.6 / 100, round(100 * CVwRfromU(136.42 / 100), 1)) [1] TRUE

—
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

Alyssa
☆

Malaysia,
2020-05-05 07:00
(1681 d 07:26 ago)

@ Helmut
Posting: # 21400
Views: 11,328

It is....

Post reply

Wow! @Elmaestro nothing can hide from you. Opss I should'nt disclose it here. :surprised:

@Helmut thanks for the explanation i will try to digest the PowerTOST calculation mentioned above.
I'm not a biostatistician.....so need some time. :cool:

Edit: Full quote removed. Please delete everything from the text of the original poster which is not necessary in understanding your answer; see also this post #5! [Helmut]

Helmut
★★★

Vienna, Austria,
2020-05-05 14:45
(1680 d 23:40 ago)

@ Alyssa
Posting: # 21401
Views: 11,401

Tutorial

Post reply

Hi Alyssa,

❝ i will try to digest the PowerTOST calculation mentioned above.

Estimation, pleeze. ;-)

❝ I'm not a biostatistician.....so need some time. :cool:

It’s not that difficult; short tutorial:

library(PowerTOST) # T/R-ratio, CVwR, and sample size from the PAR theta0 <- 1.097 CVwR <- 0.426 n <- 41 design <- "2x3x3" # Since this was a partial replicate, we # have to assume that CVwT = CVwR CV <- rep(CVwR, 2)

What was the – though irrelevant – post hoc power?

round(100 * power.scABEL(CV = CV, theta0 = theta0, design = design, n = n), 2)

Gives

Unbalanced design. n(i)=14/14/13 assumed. [1] 84.96

What’s the sample size applying all defaults (T/R-ratio 0.90, power 0.80, partial replicate design, 100,000 simulated studies)?

sampleN.scABEL(CV = CV)

Gives

+++++++++++ scaled (widened) ABEL +++++++++++ Sample size estimation (simulation based on ANOVA evaluation) --------------------------------------------- Study design: 2x3x3 (partial replicate) log-transformed data (multiplicative model) 1e+05 studies for each step simulated. alpha = 0.05, target power = 0.8 CVw(T) = 0.426; CVw(R) = 0.426 True ratio = 0.9 ABE limits / PE constraint = 0.8 ... 1.25 EMA regulatory settings - CVswitch = 0.3 - cap on scABEL if CVw(R) > 0.5 - regulatory constant = 0.76 - pe constraint applied Sample size search n power 36 0.7642 39 0.7950 42 0.8198

OK, what about other designs/powers?

# partial replicate, 3- and 4-period full replicate designs designs <- c("2x3x3", "2x2x3", "2x2x4") # desired powers target <- c(0.80, 0.85, 0.90) # assign a data.frame of results res <- data.frame(design = rep(designs, each = length(target)), target = target, n = NA, power = NA) # loop over the rows, apply the defaults: # T/R-ratio 0.90, 100,000 simulated studies for (j in 1:nrow(res)) { res[j, 3:4] <- sampleN.scABEL(CV = CV, design = res$design[j], targetpower = res$target[j], print = FALSE, details = FALSE)[8:9] } print(res, row.names = FALSE)

Gives

design target n power 2x3x3 0.80 42 0.81976 2x3x3 0.85 48 0.86341 2x3x3 0.90 57 0.90803 2x2x3 0.80 44 0.80688 2x2x3 0.85 52 0.85872 2x2x3 0.90 62 0.90439 2x2x4 0.80 28 0.80013 2x2x4 0.85 34 0.86179 2x2x4 0.90 40 0.90514

Sample sizes for the 3-period full replicate are higher than for the partial replicate. However, don’t think only about costs. The former is more informative, since you can estimate also CV_wT. Not only that you learn something about your formulation, it is useful for designing other studies. Whereas expanding the limits is based on CV_wR, the CI depends on the pooled variance of both treatments. In many cases CV_wT < CV_wR, which would give you an incentive of the sample size. Unfortunately CV_wT is never given in PARs (not of regulatory concern). I recommend to perform pilot studies always in a full replicate design to obtain this information. Example:

# variance ratios T/R ratio <- seq(0.75, 1, 0.05) # variable CVwT and constant CVwR CV <- data.frame(CVwT = mse2CV(CV2mse(CVwR)*ratio), CVwR = CVwR) res <- data.frame(CVwT = CV[, 1], CVwR = CV[, 2], var.ratio = ratio, n = NA, power = NA) # for heterogenicity (unequal variances) # use the function sampleN.scABEL.sdsims() for (j in 1:nrow(res)) { res[j, 4:5] <- sampleN.scABEL.sdsims(CV = as.numeric(CV[j, ]), design = "2x2x3", print = FALSE, details = FALSE)[8:9] } print(signif(res, 4), row.names = FALSE)

Gives

CVwT CVwR var.ratio n power 0.3650 0.426 0.75 40 0.8135 0.3778 0.426 0.80 40 0.8053 0.3902 0.426 0.85 42 0.8119 0.4024 0.426 0.90 42 0.8042 0.4143 0.426 0.95 44 0.8114 0.4260 0.426 1.00 44 0.8040

Although the expansion remains constant, with lower CV_wT you will need less subjects.

But in your case we don’t have this information and have to assume that CV_wT = CV_wR. I recommend to perform a power analysis (see the vignette) to assess the impact of deviations from our assumptions on power. Try:

# defaults: T/R-ratio 0.90, target power 0.80, # minimum acceptable power 0.70, partial replicate design res <- pa.scABE(CV = CVwR) print(res, plotit = FALSE)

Gives

Sample size plan scABE (EMA/ABEL)

 Design alpha  CVwT  CVwR theta0 theta1 theta2 Sample size Achieved power Target power

  2x3x3  0.05 0.426 0.426    0.9    0.8   1.25          42        0.81976          0.8



Power analysis

CV, theta0 and number of subjects which lead to min. acceptable power of at least 0.7:

 CV= 0.6668, theta0= 0.8718

 n = 31 (power= 0.7059)

Now:

plot(res, pct = FALSE, ratiolabel = "T/R")

[image]

Let’s explore the panels.

If the CV will be higher than assumed, we gain power because we may expand the limits more. At ~50% power starts to drop because the upper cap of scaling and the restriction of the point estimate (within 80–125%) cuts in.
If the CV will be lower than assumed, we loose power because we may expand the limits less. At 30% and below we may not expand the limits any more but at such low CVs the study will be sufficiently powered for ABE anyway.
The T/R-ratio is the most sensitive condition. If it drops from the assumed 0.90 to 0.8718 (that’s a relative deviation of just 3.13%) power will already reach our minimum acceptable 0.70. Science is a cruel mistress.
We don’t have to worry about dropouts at all.

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Alyssa
☆

Malaysia,
2020-05-08 08:48
(1678 d 05:38 ago)

@ Helmut
Posting: # 21406
Views: 10,729

Tutorial

Post reply

Dear Helmut,

Thanks for the detailed tutorial!

I was trying to follow your command and out in the powerTOST, however, i cant get the sample size estimation. May i know what has gone wrong? Thank you ~

 # partial replicate, 3- and 4-period full replicate designs

> designs <- c("2x3x3", "2x2x3", "2x2x4")

> # desired powers

> target  <- c(0.80, 0.85, 0.90)

> # assign a data.frame of results

> res     <- data.frame(design = rep(designs, each = length(target)),

+                       target = target, n = NA, power = NA)

> # loop over the rows, apply the defaults:

> # T/R-ratio 0.90, 100,000 simulated studies

> for (j in 1:nrow(res)) {

+   res[j, 3:4] <- sampleN.scABEL(CV = CV, design = res$design[j],

+                                 targetpower = res$target[j],

+                                 print = FALSE, details = FALSE)[8:9]
+ }

Error in sampleN.scABEL(CV = CV, design = res$design[j], targetpower = res$target[j],  : 

  could not find function "sampleN.scABEL"

> print(res, row.names = FALSE)

 design target  n power

  2x3x3   0.80 NA    NA

  2x3x3   0.85 NA    NA

  2x3x3   0.90 NA    NA

  2x2x3   0.80 NA    NA

  2x2x3   0.85 NA    NA

  2x2x3   0.90 NA    NA

  2x2x4   0.80 NA    NA

  2x2x4   0.85 NA    NA

  2x2x4   0.90 NA    NA

Edit: Code and results BBCoded; see also this post #10. [Helmut]

d_labes ★★★ Berlin, Germany, 2020-05-08 13:16 (1678 d 01:10 ago) @ Alyssa Posting: # 21407 Views: 10,886	library Post reply
	Dear Alyssa, ❝ I was trying to follow your command and out in the powerTOST, however, i cant get the sample size estimation. May i know what has gone wrong? Thank you ~ Did you issue a `library(PowerTOST)` before executing the rest of the code? — Regards, Detlew

Helmut
★★★

Vienna, Austria,
2020-05-08 15:08
(1677 d 23:17 ago)

@ Alyssa
Posting: # 21408
Views: 10,695

Tutorial (from the start)

Post reply

Hi Alyssa,

❝ Thanks for the detailed tutorial!

Welcome.

❝ I was trying to follow your command and out in the powerTOST, however, i cant get the sample size estimation. May i know what has gone wrong?

Your results show that you did not attach the library (see Detlew’s post). However, this part of the code alone will still not work because the CV is not specified. You would get:

Error in sampleN.scABEL(CV = CV, design = res$design[j], targetpower = res$target[j],  : 

  object 'CV' not found

I assumed that you will execute the entire code of the tutorial step by step. If you want only this part and/or the power analysis at the end, start with:

library(PowerTOST) CVwR <- 0.426 CV <- rep(CVwR, 2)

PS: R-terminology.

A package contains functions, man-pages, a DESCRIPTION-file (mandatory); data, vignettes, PDFs, … (optional).
Hence, you find Packages in the menu-bar of the R-console. IMHO, in the submenu it should rather state Load library… instead of Load package… because:
A library is a locally installed package.
Therefore, the command to attach it is library().

For beginners I recommend Load package…. Say, you want to work with “foo”. If “foo” is not installed yet, it will be missing in the list. If you try library(foo) in the R-console and “foo” is not installed you will get:
Error in library(foo) : there is no package called ‘foo’

The safe way to check whether a package “foo” is already installed (and if not, download it from a CRAN-mirror & install it) and attach the library is:
p <- "foo" i <- p %in% installed.packages() if (length(p[!i]) > 0) install.packages(p[!i]) library(p, character.only = TRUE)

—
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

Alyssa
☆

Malaysia,
2020-05-12 06:05
(1674 d 08:21 ago)

@ Helmut
Posting: # 21426
Views: 10,269

Tutorial (from the start)

Post reply

Hi Helmut,

Yeah, you are right, i din't install powerTOST. :-|

Once i installed and i follow from the the start, i managed to get up to :-

Sample size search

 n     power

36   0.7642

39   0.7950

42   0.8198

Then i continued with the following command but it appeared error:-

> # partial replicate, 3- and 4-period full replicate designs

> designs <- c("2x3x3", "2x2x3", "2x2x4")

> # desired powers

> target  <- c(0.80, 0.85, 0.90)

> # assign a data.frame of results

> res     <- data.frame(design = rep(designs, each = length(target)),

+                       target = target, n = NA, power = NA)

> # loop over the rows, apply the defaults:

> # T/R-ratio 0.90, 100,000 simulated studies

> for (j in 1:nrow(res)) {

+   res[j, 3:4] <- sampleN.scABEL(CV = CV, design = res$design[j],

+                                 targetpower = res$target[j],

+                                 print = FALSE, details = FALSE)[8:9]
+ }

Error in match.arg(design) : 'arg' must be NULL or a character vector

> print(res, row.names = FALSE)

 design target  n power

  2x3x3   0.80 NA    NA

  2x3x3   0.85 NA    NA

  2x3x3   0.90 NA    NA

  2x2x3   0.80 NA    NA

  2x2x3   0.85 NA    NA

  2x2x3   0.90 NA    NA

  2x2x4   0.80 NA    NA

  2x2x4   0.85 NA    NA

  2x2x4   0.90 NA    NA

May i know what is this error about? Thank you.

Edit: Code and results BBCoded; see also this post #10. [Helmut]

Helmut
★★★

Vienna, Austria,
2020-05-12 13:49
(1674 d 00:36 ago)

@ Alyssa
Posting: # 21427
Views: 10,305

Tracking down error

Post reply

Hi Alyssa,

❝ Then i continued with the following command but it appeared error: […]

Let’s try to figure it out. Execute this code:

library(PowerTOST)

CVwR    <- 0.426

CV      <- rep(CVwR, 2)

designs <- c("2x3x3", "2x2x3", "2x2x4")

target  <- c(0.80, 0.85, 0.90)

res     <- data.frame(design = rep(designs, each = length(target)),

                      target = target, n = NA, power = NA)

for (j in 1:nrow(res)) {

  res[j, 3:4] <- sampleN.scABEL(CV = CV, design = res$design[j],

                                targetpower = res$target[j],

                                print = FALSE, details = FALSE)[8:9]


}

print(res, row.names = FALSE)

If it works now, fine. If you get the same error, try:

res[1, 3:4] <- sampleN.scABEL(CV = CV, design = "2x3x3",

                              targetpower = 0.8, print = FALSE,

                              details = FALSE)[8:9]


print(res[1, ], row.names = FALSE)

Expected:

 design target  n   power

  2x3x3    0.8 42 0.81976

If you get an error (or something else), let’s see what you have installed. Try this:

p <- c("PowerTOST", "mvtnorm", "stats", "utils",

       "graphics", "grDevices", "cubature",

       "TeachingDemos", "crossdes", "devtools")

i <- data.frame(name = c("R", p),

                version = c(noquote(as.character(getRversion())),

                            rep(NA, length(p))),

                status = c("-", rep("required", 8),

                           rep("suggested", 2)))

for (j in 2:nrow(i)) {

  i[j, 2] <- noquote(as.character(packageVersion(i[j, 1])))

}

print(i, row.names = FALSE)

On my machine:

          name    version    status

             R      4.0.0         -

     PowerTOST 1.4.9.9999  required

       mvtnorm      1.1.0  required

         stats      4.0.0  required

         utils      4.0.0  required

      graphics      4.0.0  required

     grDevices      4.0.0  required

      cubature      2.0.4  required

 TeachingDemos       2.12  required

      crossdes      1.1.1 suggested

      devtools      2.3.0 suggested

If you have [image]

<4.0.0, I suggest to update. Then copy the entire library-folder of the old installation over the new installation. When asked whether newer files should be overwritten, answer No. Open the console of the new [image]

and execute:
update.packages(checkBuilt = TRUE, ask = FALSE)
Once done, it should look similar to mine. 1.4.9.9999 is the development version of PowerTOST; on your machine it should be 1.4.9. If one is missing (<NA> in the column version), download/install it.
To get more information about you installation, execute:
devtools::session_info()
On my machine:

- Session info ---------------------------------------------------------------

 setting  value                       

 version  R version 4.0.0 (2020-04-24)

 os       Windows 7 x64 SP 1          

 system   x86_64, mingw32             

 ui       Rgui                        

 language EN                          

 collate  German_Germany.1252         

 ctype    German_Germany.1252         

 tz       Europe/Vienna               

 date     2020-05-12                  



- Packages -------------------------------------------------------------------

 package       * version    date       lib source        

 assertthat      0.2.1      2019-03-21 [1] CRAN (R 4.0.0)

 backports       1.1.6      2020-04-05 [1] CRAN (R 4.0.0)

 callr           3.4.3      2020-03-28 [1] CRAN (R 4.0.0)

 cli             2.0.2      2020-02-28 [1] CRAN (R 4.0.0)

 crayon          1.3.4      2017-09-16 [1] CRAN (R 4.0.0)

 cubature        2.0.4      2019-12-04 [1] CRAN (R 4.0.0)

 desc            1.2.0      2018-05-01 [1] CRAN (R 4.0.0)

 devtools        2.3.0      2020-04-10 [1] CRAN (R 4.0.0)

 digest          0.6.25     2020-02-23 [1] CRAN (R 4.0.0)

 ellipsis        0.3.0      2019-09-20 [1] CRAN (R 4.0.0)

 fansi           0.4.1      2020-01-08 [1] CRAN (R 4.0.0)

 fs              1.4.1      2020-04-04 [1] CRAN (R 4.0.0)

 glue            1.4.0      2020-04-03 [1] CRAN (R 4.0.0)

 magrittr        1.5        2014-11-22 [1] CRAN (R 4.0.0)

 memoise         1.1.0      2017-04-21 [1] CRAN (R 4.0.0)

 mvtnorm         1.1-0      2020-02-24 [1] CRAN (R 4.0.0)

 pkgbuild        1.0.8      2020-05-07 [1] CRAN (R 4.0.0)

 pkgload         1.0.2      2018-10-29 [1] CRAN (R 4.0.0)

 PowerTOST     * 1.4.9.9999 2020-05-11 [1] local         

 prettyunits     1.1.1      2020-01-24 [1] CRAN (R 4.0.0)

 processx        3.4.2      2020-02-09 [1] CRAN (R 4.0.0)

 ps              1.3.3      2020-05-08 [1] CRAN (R 4.0.0)

 R6              2.4.1      2019-11-12 [1] CRAN (R 4.0.0)

 Rcpp            1.0.4.6    2020-04-09 [1] CRAN (R 4.0.0)

 remotes         2.1.1      2020-02-15 [1] CRAN (R 4.0.0)

 rlang           0.4.6      2020-05-02 [1] CRAN (R 4.0.0)

 rprojroot       1.3-2      2018-01-03 [1] CRAN (R 4.0.0)

 sessioninfo     1.1.1      2018-11-05 [1] CRAN (R 4.0.0)

 TeachingDemos   2.12       2020-04-07 [1] CRAN (R 4.0.0)

 testthat        2.3.2      2020-03-02 [1] CRAN (R 4.0.0)

 usethis         1.6.1      2020-04-29 [1] CRAN (R 4.0.0)

 withr           2.2.0      2020-04-20 [1] CRAN (R 4.0.0)



[1] D:/Program Files/R/R-4.0.0/library

Likely your list will be shorter; please post here what you got.

PS: When you post code/output, please embed it within the respective BBCode. With active JavaScript highlight the text in the message-area and click on the Button Code. THX.

—
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

d_labes
★★★

Berlin, Germany,
2020-05-13 13:34
(1673 d 00:51 ago)

@ Helmut
Posting: # 21431
Views: 10,109

Tracking down error: stringsAsFactors

Post reply

Dear Helmut, dear Alyssa!

❝ ❝ Then i continued with the following command but it appeared error: […]

❝ Let’s try to figure it out. Execute this code:

❝ ...

❝ res <- data.frame(design = rep(designs, each = length(target)),

❝ target = target, n = NA, power = NA)

This line is the casus knacktus!
The error
Error in match.arg(design) : 'arg' must be NULL or a character vector
means that the design argument in the call of sampleN.scABEL() is not character.
Reason:
Before R4.0.0 the optional argument stringsAsFactors in the call of data.frame() was TRUE. Changed in R4.0.0 to FALSE.
Check this in your installation via
default.stringsAsFactors()
If you get TRUE as answer change

res     <- data.frame(design = rep(designs, each = length(target)),

                      target = target, n = NA, power = NA,

                      stringsAsFactors = FALSE)

and than I'm sure that it works.

All the best

—
Regards,

Detlew

Helmut ★★★ Vienna, Austria, 2020-05-13 14:07 (1673 d 00:18 ago) @ d_labes Posting: # 21432 Views: 10,141	R <4.0.0 Post reply
	Dear Detlew, oh, f*k! I forgot that users may not have updated yet. Shall we add a line to `PowerTOST`’s functions as a precaution? `if (is.factor(design)) design <- as.character(design)` — 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

ElMaestro ★★★ Denmark, 2020-05-13 16:18 (1672 d 22:08 ago) @ Helmut Posting: # 21433 Views: 9,979	R <4.0.0 Post reply
	Doh! — Pass or fail! ElMaestro

Helmut ★★★ Vienna, Austria, 2020-05-13 16:21 (1672 d 22:04 ago) @ ElMaestro Posting: # 21434 Views: 10,008	R <4.0.0? Post reply
	^{© Helmut Schütz 2012} »Dead as a Dodo« — 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

d_labes
★★★

Berlin, Germany,
2020-05-13 20:50
(1672 d 17:35 ago)

@ Helmut
Posting: # 21435
Views: 10,010

PowerTOST’s functions with as.character(design)

Post reply

Dear Helmut,

❝ oh, f**k! I forgot that users may not have updated yet.

Don't worry. Many R users are fallen or will fall into this trap I think, amongst others me.

❝ Shall we add a line to PowerTOST’s functions as a precaution?

❝ if (is.factor(design)) design <- as.character(design)

Not a bad idea. But don't really know if this is absolutely necessary and if it pays the effort.

—
Regards,

Detlew

Helmut
★★★

Vienna, Austria,
2020-05-13 20:55
(1672 d 17:30 ago)

@ d_labes
Posting: # 21436
Views: 9,952

PowerTOST’s functions with as.character(design)

Post reply

Dear Detlew,

❝ Don't worry. Many R users are fallen or will fall into this trap I think, amongst others me.

Count me in.

❝ ❝ Shall we add a line to PowerTOST’s functions as a precaution?

❝ ❝ if (is.factor(design)) design <- as.character(design)

❝

❝ Not a bad idea. But don't really know if this is absolutely necessary and if it pays the effort.

It’s easy for us. But you’re right: Duno how many times a user would set up a data.frame and call the design-argument in a loop.

—
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

Alyssa
☆

Malaysia,
2020-05-15 07:08
(1671 d 07:17 ago)

@ Helmut
Posting: # 21438
Views: 10,032

Tracking down error

Post reply

Hi Helmut and d_labes,

Sorry for late reply because i din't receive email notification i thought no one has reply to my query yet until i login just now.

I followed your instructions and yeah...finally i could get the sample size estimation !!!! so happy! :rotfl:

I dint get to try the below command because i have updated the programme to R 4.0.0

res     <- data.frame(design = rep(designs, each = length(target)),

                      target = target, n = NA, power = NA,

                      stringsAsFactors = FALSE)

When i run devtools::session_info(), this is what i get:-

- Session info ---------------------------------------------------------------

 setting  value                       

 version  R version 4.0.0 (2020-04-24)

 os       Windows 10 x64              

 system   x86_64, mingw32             

 ui       Rgui                        

 language (EN)                        

 collate  English_Malaysia.1252       

 ctype    English_Malaysia.1252       

 tz       Asia/Kuala_Lumpur           

 date     2020-05-15                  



- Packages -------------------------------------------------------------------

 package       * version date       lib source        

 assertthat      0.2.1   2019-03-21 [1] CRAN (R 4.0.0)

 backports       1.1.6   2020-04-05 [1] CRAN (R 4.0.0)

 callr           3.4.3   2020-03-28 [1] CRAN (R 4.0.0)

 cli             2.0.2   2020-02-28 [1] CRAN (R 4.0.0)

 crayon          1.3.4   2017-09-16 [1] CRAN (R 4.0.0)

 cubature        2.0.4   2019-12-04 [1] CRAN (R 4.0.0)

 desc            1.2.0   2018-05-01 [1] CRAN (R 4.0.0)

 devtools        2.3.0   2020-04-10 [1] CRAN (R 4.0.0)

 digest          0.6.25  2020-02-23 [1] CRAN (R 4.0.0)

 ellipsis        0.3.0   2019-09-20 [1] CRAN (R 4.0.0)

 fansi           0.4.1   2020-01-08 [1] CRAN (R 4.0.0)

 fs              1.4.1   2020-04-04 [1] CRAN (R 4.0.0)

 glue            1.4.0   2020-04-03 [1] CRAN (R 4.0.0)

 htmltools       0.4.0   2019-10-04 [1] CRAN (R 4.0.0)

 knitr           1.28    2020-02-06 [1] CRAN (R 4.0.0)

 magrittr        1.5     2014-11-22 [1] CRAN (R 4.0.0)

 memoise         1.1.0   2017-04-21 [1] CRAN (R 4.0.0)

 mvtnorm         1.1-0   2020-02-24 [1] CRAN (R 4.0.0)

 pkgbuild        1.0.8   2020-05-07 [1] CRAN (R 4.0.0)

 pkgload         1.0.2   2018-10-29 [1] CRAN (R 4.0.0)

 PowerTOST     * 1.4-9   2019-12-19 [1] CRAN (R 4.0.0)

 prettyunits     1.1.1   2020-01-24 [1] CRAN (R 4.0.0)

 processx        3.4.2   2020-02-09 [1] CRAN (R 4.0.0)

 ps              1.3.3   2020-05-08 [1] CRAN (R 4.0.0)

 R6              2.4.1   2019-11-12 [1] CRAN (R 4.0.0)

 Rcpp            1.0.4.6 2020-04-09 [1] CRAN (R 4.0.0)

 remotes         2.1.1   2020-02-15 [1] CRAN (R 4.0.0)

 rlang           0.4.6   2020-05-02 [1] CRAN (R 4.0.0)

 rprojroot       1.3-2   2018-01-03 [1] CRAN (R 4.0.0)

 sessioninfo     1.1.1   2018-11-05 [1] CRAN (R 4.0.0)

 TeachingDemos   2.12    2020-04-07 [1] CRAN (R 4.0.0)

 testthat        2.3.2   2020-03-02 [1] CRAN (R 4.0.0)

 tufte           0.6     2020-05-08 [1] CRAN (R 4.0.0)

 usethis         1.6.1   2020-04-29 [1] CRAN (R 4.0.0)

 withr           2.2.0   2020-04-20 [1] CRAN (R 4.0.0)

 xfun            0.13    2020-04-13 [1] CRAN (R 4.0.0)



[2] C:/Program Files/R/R-4.0.0/library

Thank you and Cheers~~ :-D

Helmut
★★★

Vienna, Austria,
2020-05-15 12:52
(1671 d 01:33 ago)

@ Alyssa
Posting: # 21439
Views: 9,846

All’s well that ends well.

Post reply

Hi Alyssa,

❝ Sorry for late reply because i din't receive email notification …

You didn’t tick the box
☐ E-mail notification if there has been a reply to this message
See also there.
However, that’s not ideal because you will miss other replies (i.e., not directly addressed to you). Consider to subscribe the thread.

❝ … i thought no one has reply to my query yet until i login just now.

You don’t have to login to read posts in the forum. I suggest to bookmark the Latest Posts.

❝ I followed your instructions and yeah...finally i could get the sample size estimation !!!! so happy! :rotfl:

❝ When i run devtools::session_info(), this is what i get: […]

Perfect!

❝ Thank you and Cheers~~ :-D :party:

You are welcome!

—
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

Sample size calculation (Pilot study result vs literature) [Power / Sample Size]

Sample size calculation (Pilot study result vs literature)

Sample size calculation (Pilot study result vs literature)

Estimation of CVw and/or CVwR

Estimation of CVw and/or CVwR

Mea culpa!

It must be Myco

It is, it is!

It is....

Tutorial

Tutorial

library

Tutorial (from the start)

Tutorial (from the start)

Tracking down error

Tracking down error: stringsAsFactors

R <4.0.0

R <4.0.0

R <4.0.0?

PowerTOST’s functions with as.character(design)

PowerTOST’s functions with as.character(design)

Tracking down error

All’s well that ends well.