Bioequivalence and Bioavailability Forum

xtianbadillo
☆

Mexico,
2014-12-12 08:27
(3416 d 13:55 ago)

Posting: # 14060
Views: 5,402

How to use Bear for sample size calculation for Xover study [🇷 for BE/BA]

How to use Bear for sample size calculation for Crossover study 2x2, power=80, CVintra=.16, BE margins=80-125

description_size

function (parallel = FALSE) 

{

  cat("\n")

  cat("****************************************************************************\n")

  cat("                            Required data                                   \n")

  cat("----------------------------------------------------------------------------\n")

  cat(" 1. Theta (%): the target ratio in average BA between the two formulations\n")

  cat("    expressed in percentage of the average reference BA.                    \n")

  cat(" 2. Power (%): the least statistical power to detect (1-Power)          \n")

  cat("    differences between the Test and the Reference formulation.             \n")

  if (parallel) {

    cat(" 3. CV (%): the inter-subject coefficient of variation for parallel  \n")

    cat("    study.                                                                  \n")

  }

  else {

    cat(" 3. CV (%): the intra-subject coefficient of variation for crossover  \n")

    cat("    (or replicate crossover) study.                                         \n")

  }

  cat(" 4. Lower acceptance limit represents the lower BE criteria to be accepted; \n")

  cat("    the upper acceptance limit is equal to 1/(lower acceptance limit).     \n")

  cat("    Usually the lower limit can be 80.000% or, 70.000% in some cases.      \n")

  cat("****************************************************************************\n")

  cat("\n")

  readline(" Press Enter to proceed...")

}

<environment: namespace:bear>

Helmut
★★★

Vienna, Austria,
2014-12-12 14:50
(3416 d 07:32 ago)

@ xtianbadillo
Posting: # 14063
Views: 5,070

Sample size estimation in bear and PowerTOST

Post reply

Hi Christian,

❝ How to use Bear for sample size calculation for Crossover study 2x2, power=80, CVintra=.16, BE margins=80-125

Not sure why you posted a function-dump. Did you get an error?
With your values I got:

---------------------------------------------------------------------------

                            <<Sample Size Estimation>>                     

                                                                           

  Upper acceptance limit = 125 %

  Lower acceptance limit = 80 %

      Expected ratio T/R = 95.00 %

            Target power = 80.00 %

        Intra-subject CV = 16.0 %



  study     2x2x2        2x2x3       2x2x4 

  design  crossover   replicated   replicated 

 ------- ----------- ------------ ------------ 

    N        14          12           8



         Estimated power =   84.866 %

--------------------------------------------------------------------------



**Ref.:

 1. Hauschke D, Steinijans VW, Diletti E and Burke M.  Sample size         

    determination for bioequivalence assessment using a multiplicative model.

    Journal of Pharmacokinetics and Biopharmaceutics. 20, 557-561 (1992).  

 2. Julious SA. Tutorial in biostatistics: Sample sizes for clinical trials

    with normal data.  Statistics in Medicine. 23, 1921-1986 (2004).       

 3. Hauschke D, Steinijans VW and Pigeot I.  Bioequivalence studies in drug

    development methods and applications. John Wiley & Sons, New York      

    (2007).                                                                



 Note: The algorithms we use here have been validated with Dr. David Dubins'

 FARTSSIE, which is a very excellant Excel VBA program for many sample size 

 estimation functions.  Website: http://individual.utoronto.ca/ddubins.    



--------------------------------------------------------------------------

bear is a nice package, but the sample size estimation is limited to 2×2×2 crossovers and parallel designs. The sample sizes for the replicate designs are given as ≥75% (2×2×3) and ≥50% (2×2×4) of the 2×2×2 – which is only a rule of thumb.

If you want more flexibility (e.g., designs, α different from 0.05, sample sizes for FDA’s RSABE and EMA’s ABEL,…) consider package PowerTOST:

library(PowerTOST)

sampleN.TOST(alpha=0.05, targetpower=0.8, theta0=0.95, theta1=0.8,

             theta2=1.25, CV=0.16, design="2x2")



+++++++++++ Equivalence test - TOST +++++++++++

            Sample size estimation

-----------------------------------------------

Study design:  2x2 crossover 

log-transformed data (multiplicative model)



alpha = 0.05, target power = 0.8

BE margins        = 0.8 ... 1.25 

Null (true) ratio = 0.95,  CV = 0.16



Sample size (total)

 n     power

14   0.848665

Note that α 0.05, target power 0.8, T/R 0.95, acceptance range 0.80–1.25, and 2×2×2 are the function’s defaults – which are applicable in your case. Hence, you will get the same output by simply using sampleN.TOST(CV=0.16).

—
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

nobody nothing 2014-12-12 15:24 (3416 d 06:58 ago) @ Helmut Posting: # 14064 Views: 4,651	Sample size estimation in bear and PowerTOST Post reply
	Hi! Important: "Expected ratio T/R = 95.00 %" ...sometime 90% might be more comfortable, depending on the kind of formulation tested... — Kindest regards, nobody

Helmut
★★★

Vienna, Austria,
2014-12-12 15:49
(3416 d 06:33 ago)

@ nobody
Posting: # 14066
Views: 4,613

Sample size estimation in bear and PowerTOST

Post reply

Hi nobody,

❝ "Expected ratio T/R = 95.00 %"

❝

❝ ...sometime 90% might be more comfortable, depending on the kind of formulation tested...

Sure – and possible in both packages. In PowerTOST 95% is default in most functions, but 90% for HVDs and 97.5% for NTIDs.

—
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

yjlee168
★★★

Kaohsiung, Taiwan,
2014-12-13 18:26
(3415 d 03:56 ago)

@ Helmut
Posting: # 14068
Views: 4,547

Sample size estimation in bear and PowerTOST

Post reply

Dear Helmut & Christian,

Helmut: thank you so much for your replied message. Totally agree. :clap:

Christian: yes, I also strongly recommend PowerTOST (see Helmut's messages) for sample size estimation with BE study. Bear is a menu-driven R package. After loading bear with library(bear), just type go() under R console to run bear. Remember to make sure the current setting is what you need first.

❝ Not sure why you posted a function-dump. Did you get an error?

—
All the best,
-- Yung-jin Lee
bear v2.9.1:- created by Hsin-ya Lee & Yung-jin Lee
Kaohsiung, Taiwan https://www.pkpd168.com/bear
Download link (updated) -> here

How to use Bear for sample size calcu­lation for Xover study [🇷 for BE/BA]

Sample size estimation in bear and PowerTOST

Sample size estimation in bear and PowerTOST

Sample size estimation in bear and PowerTOST

Sample size estimation in bear and PowerTOST

How to use Bear for sample size calculation for Xover study [🇷 for BE/BA]