Handling BLOQ values (Fisher Info etc.) [Bioanalytics]

posted by mittyri – Russia, 2019-03-16 15:41 (1723 d 09:51 ago) – Posting: # 20038
Views: 5,675

Dear All,

some time ago I was pleased to be invited on the session with Roger Jelliffe.
Some cites from one of the lectures:

Labs have been used to presenting a result only to generate a number, with a percent error, for evaluation by themselves or a clinician.
Labs have NOT been used to having their data FITTED using modern quantitative modeling methods which require one to evaluate credibility of a measurement correctly. That is the problem. Labs have been used to CV% only. CV% is simply not suitable for today’s modern quantitative modeling methods.
As measurement gets lower, CV% increases, and becomes infinite at zero. It fails. Always has, always will.
When CV% reaches some value, lab people think they must censor (withhold) results below this. Report “less than……”
They invent the LLOQ. LLOQ is their ILLUSION!
NO NEED for this!
Because SD, variance, and weight (1/var) are all finite throughout, all the way down to and including the zero blank.
Fisher Information quantifies the credibility of a lab measurement.
Fisher Information = 1/Var
So need to know, or have a good estimate, of the SD of every serum level.
Labs always get SD anyway, to get the CV%.
Then, Var = SD2
And Credibility = Fisher info = 1/Var.
Assay CV% versus correct weight, Fisher Info
Assume, for example, 10% assay CV
If conc = 10, SD = 1, var = 1, weight = 1
If conc = 20, SD = 2, var = 4, weight = ¼ - Aha!
So a constant linear % error (the assay CV%) is NOT the correct measure of the error!
Never was, never will be.
As conc approaches zero, CV% approaches infinity.
But assay SD, var, weight are always finite. Fisher info is the correct measure of assay precision.
Also, no need for any ILLUSORY LLOQ!

I've also seen some similar opinion on nmusers maillist (may be it was Nick Holford).

My question is are there any steps forward in that direction?
Even in analytical methodology (say QC samples): isn't possible to handle that values (as they are) for example for the mean evaluation for some level of QC sample?

PS: sorry, my bioanalytics skills are far away from acceptable level :-|

Kind regards,

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