Bioequivalence and Bioavailability Forum

Dear Helmut!

❝ ❝ But your implementation of the analytical error via log-normal distribution seems correct for me.

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❝ Really? Im not sure about meanlog=0 since dlnorm(1)==dnorm(0); shouldn’t I rather use meanlog=1?^*

If it comes to the log-normal I prefer always to work in the log domain, i.e. simulate the errors via normal distribution add it to the log-transformed theoretical value and than transform it back. The syntax of *lnorm() functions (which values to use as meanlog, meansd) is too complicated for my mind . Eventually this helps to figure out.

❝ ❝ What I absolutely don't understand is the "... constant term ...". What is it good for . This is only a shift in the concentration levels constant over the whole curve and also for all simulated profiles the same, if I understand. But nothing like a random term as errors usually are deemed for.

❝

❝ Me too. I don't get the idea as well. Maybe it’s time to ask László.

That seems a very good idea. Which László ever .

❝ ❝ BTW: Why do you think you have screwed up something? Because the scatter in the simulated data is too smooth compared to real data .

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❝ Exactly. Also I don’t get the point why I should go with a log-normal here (noise is not necessarily positive). Analytical error is normal, IMHO.

(Emphasis by me) This is a very good question, able to battle on over long evenings at beer with smoking heads .

Regarding positive noise via log-normal you mix up somefink here, I think. The log-normal lends to positive or negative errors in the log-domain which back-transformed gives values greater or lower than the theoretical one in a multiplicative fashion.

The log-normal lends to variances (in the original domain) which are proportional to the values itself (higher variability at higher values). Maybe this is not the correct behavior since in bioanalysis it is often so that the errors are biggest at the lowest concentration. But here you are the expert .