GM Junior India, 20181102 06:25 Posting: # 19522 Views: 367 

Dear Dlabes, I am trying to calculate the sample size for replicate design using R software with sampleN.RSABE. With keen interest to know about the functionality of sampleN.RSABE, I have searched in the web. I got some backend code of this function from Github (see link). I am a beginner of R software, but I tried to understand this. There are some intermediate functions like .sampleN0, .sampleN0.2, .power.RSABE are involved. I got the informationn of first two functions from the same github. but not the third one. As you are the auther of this function, am directly requesting you. Kindly explain or suggest any link about this function. Thank you in advance. — Best Regards, GM 
Helmut Hero Vienna, Austria, 20181102 10:50 @ GM Posting: # 19523 Views: 326 

Hi GM, not Detlew but answering anyway. » I am trying to calculate the sample size for replicate design using R software with sampleN.RSABE. So why are you not simply using it?
?sampleN.RSABE in the Rconsole / RStudio’s console or consult the online manual.» I am a beginner of R software If you want to dive into the code, be aware of R’s steep learning curve. » […] but I tried to understand this. OK, this is a laudable. » There are some intermediate functions like .sampleN0, .sampleN0.2, .power.RSABE are involved. » I got the informationn of first two functions from the same github. but not the third one. .power.RSABE is contained in power_RSABE2L_isc.R .— Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. ☼ Science Quotes 
GM Junior India, 20181103 05:09 @ Helmut Posting: # 19525 Views: 295 

Hi Helmut, » not Detlew but answering anyway. Thank you for quick reply at every time. » So why are you not simply using it? »
?sampleN.RSABE in the Rconsole / RStudio’s console or consult the online manual.As this is a replicated study, there are only few articles for calculating the sample size. There is no clear information about this. So that, I suppose to understand concept through Rprogram. I have few more doubts, regarding the terms CVswitch, r_const and pe_constr. I thought CVswitch=0.30 for both FDA and EMA, r_const=0.25 and 0.760 for FDA and EMA respectively and don't know about the pe_constr. Kindly correct me if I am wrong... Thank you in advance. — Best Regards, GM 
Helmut Hero Vienna, Austria, 20181103 13:04 @ GM Posting: # 19526 Views: 279 

Hi GM, » As this is a replicated study, there are only few articles for calculating the sample size. If referencescaling (RSABE, ABEL) is concerned, do you know any article except the two Lászlós’?^{1} We need simulations because the methods are implicitly sequential. Like in any framework (following a decision scheme) an analytical solution for power does not exist.
However, the sample size estimation for ABE in replicate designs is straightforward (i.e., does not require simulations). Use sampleN.TOST() instead.» There is no clear information about this. So that, I suppose to understand concept through Rprogram. Tóthfalusi et. al.^{2} is a good starting point. Other references are given in the manpages of PowerTOST .» I thought CVswitch=0.30 for both FDA and EMA, … Correct. » … r_const=0.25 and 0.760 for FDA and EMA respectively … Not quite. The switching condition θ_{s} (aka regulatory constant) is based on the regulatory standardized variation σ_{w0}. For the FDA σ_{w0} = 0.25 and for the EMA based on CV_{wR} 30% as σ_{w0} = √log(0.30²+1) = 0.2935604. Then θ_{s} = log(1.25)/σ_{w0} = 0.8925742 (FDA) and 0.7601283 (EMA). Note that σ_{w0} 0.25 is explicitly given by the FDA and therefore, the regulatory constant in PowerTOST is used in full precision.* On the other hand, the EMA requires the rounded 0.760 (termed k in the GL) and an upper cap for scaling at CV_{wR} 50% (EL 69.84–143.19%). Check the conditions:
» … and don't know about the pe_constr. It was introduced by all agencies following suggestions by Les Benet (see this post). It is “political” and leads to statistical troubles (essentially we are truncating the distribution and the entire concept is built on sand).
reg_const("HC") .
— Cheers, Helmut Schütz The quality of responses received is directly proportional to the quality of the question asked. ☼ Science Quotes 