CVtotal ≠ CVinter № 42 [Power / Sample Size]

posted by Helmut Homepage – Vienna, Austria, 2013-01-11 18:59 (4916 d 13:01 ago) – Posting: # 9829
Views: 6,288

Hi Angus!

❝ It seems that the English language description of the output you get is poor. Both WinNonlin and NCSS are from here.


You are absolutely right. Sometimes manuals increase confusion. PHC/WNL states in one place “variability” meaning standard deviation in one setting and variance in an other. :angry:

❝ The lack of clarity is the major problem and of course statistics is a confusing subject especially when the terms used are not well described or ccan be equivalent.


You are not alone.

❝ I compared the output from WinNonlin and NCSS: the variance term is exactly the same. WinNonlin calls the variance term "variance residual" NCSS provides a row in a Table and calls it the Error term of the 2 treatments A and B and it refers to it as the Mean Square Error (MSE) in another part of the output.


The latter is the most commonly one given by other software.

❝ They are in my example exactly the same number.


OK, fine. I was concerned about the way WNL calculates the confidence interval – which assumes equal variances. Only the degrees of freedom of the t-test have to corrected by the Satterthwaite approximation. If you want to estimate the sample size for another study you need just the residual variance, aka mean square error.

❝ So it seems that indeed it is the total error or total variance not the inter subject variance we have.


Yep.

❝ So that means that the value we have is a convolution of the intersubect error and intrasubject error?


I would rather call it a combination. Convolution smells of

❝ And as previously referred to is higher than intersubject variance so using it provides a higher estimate of sample size.


Almost. You could separate inter- and and intrasubject variance only in a cross-over study (see here). If you have data of a parallel study and want to plan another one simply use the error term. Call it CVtotal if you are a nitpicker like me.

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