Concentration Statistics - BQL substitution [General Sta­tis­tics]

posted by ElMaestro  – Denmark, 2020-07-08 11:35 (237 d 00:25 ago) – Posting: # 21657
Views: 2,046

Hi all,

I like the discussion, but I wonder are BLQ's really such an issue?
I mean, we have some rules which we can scientifically debate, but regardless of whether we like them scientifically, do the rules as we know them today lead to actual trouble? :-)

Just think about it, one perspective: Regulators mandate the use of the normal linear model in BE, and everyone knows that model may be right or wrong, probably it is wrong to a varying degree in all datasets. Sometimes we even know from KS tests or SW tests or God knows what, that the assumption of normality is outright wrong, yet we have to use the assumption of normality anyway. Is it a problem that an unknown proportion of studies definitely do not meet the assumptions, and that those that don't do so with a magnitude and nature whose consequences cannot be assessed?
No, actually BE seems to work rather fine in spite of all this. At least as I see it. Somehow I see the BLQ discussion the same way. Yes, it may not be optimal, but hey it provides a strict and well-defined way forward where everyone can easily reproduce everything from raw data. Last time I looked people taking generics were not dropping dead in the streets :-)

A biased estimator may indeed be a useful estimator.
A BLQ rule which is not scientifically optimal when looked at through the keyhole may still in a wider perspective be a good BLQ rule.
How about the AUCinf extrapolation rule of 80%? Is it a complete disaster that it isn't 83%? I mean at the end of the day, the smaller the extrapolated area the better we know the profile from the get-go, so 83% must be better than 80%. And so forth.

Just trying to put BE things into a perspective here. :-)

Pass or fail!

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