Bioequivalence and Bioavailability Forum

Hi Ken Peh,

I agree with Ohlbe, and I have some add'l comments.

❝ I. Besides the accuracy values, is regression coefficient value important, eg. R square >0.99 ? Should both be considered as acceptance criteria ? The accuracy and R square values.

When we talk R squared then we usually interpret it as the quantification of explanatory variation of the model. If we have an R sq. of 0.99 then it means our model explains 99% of the scatter in the data (at least in comparison to a non-model - a straight line through the mean).
Now, if scatter per se is a problem then we have precision to deal with that so we likely do not need R sq. also*. But I'd say, I see no issue in using R sq. internally at your CRO as a quality criterion (it will help you pass precision), just like column age, grid uptime etc.

❝ II. If we have 8 calibrators, from back calculated concentration of the calibration standards, we found 2 calibration standards failed accuracy. One of calibration standard was LLOQ. To keep the LLOQ, instead of dropping the failed LLOQ, reject another calibration standard which passes the accuracy and reevaluate the calibration curve. As a result, 7 calibration standards pass. Is this the right approach ??

Dropping a passing calibrator to make another one pass might not be a good idea. In a sense someone might say you should care for scientific quality rather than for the LLOQ itself.

Bob is 175 cm tall, Jim is 180 cm tall and Matt is 185 cm tall. What is their average height**?

Best regards,
EM.

















* Until someone at a Crystal City meeting comes up with the idea to also require a R sq. of 0.99 or better for whatever reason, then suddenly everyone wants it.
** 177.5 cm, because we do not consider Matt.