compromise [Bioanalytics]
Dear ElMaestro,
Well, cough... The reason is usually something like "this is what we have in our SOP", or "we always did it like that", or "that's how we did it in my previous company", and the same rule is used for all analytes and methods whatever is seen during method development and validation...
There are pros and cons in both methods, I'd say. The 50 % in the EMA guideline is probably too high, but with a log placement the MQC gets rather low, and leaves a huge gap between MQC and HQC. With a log placement of calibrators, there is also a huge gap between the HLOQ sample and the one before. It may not be a problem with a linear response, but with a quadratic equation this gap between calibration samples does not help to fit the curve (where does it become non-linear ?) and the QCs may not help to see inaccuracies.
Personally I would set the MQC around 1/3 of the ULOQ, but I have strictly no data or rationale to defend it.
In any case there may be a need to adjust the concentration of the QC samples based on the concentrations found in the subject samples after the first few runs, if the calibration range is not optimal.
❝ If you are using evenly spaced calibrators (at least at the upper end) then you have a reason for doing so.
❝ If you are using 'logarithmic' calibrators (Hötzi's expression) then you have a reason for doing so.
Well, cough... The reason is usually something like "this is what we have in our SOP", or "we always did it like that", or "that's how we did it in my previous company", and the same rule is used for all analytes and methods whatever is seen during method development and validation...
There are pros and cons in both methods, I'd say. The 50 % in the EMA guideline is probably too high, but with a log placement the MQC gets rather low, and leaves a huge gap between MQC and HQC. With a log placement of calibrators, there is also a huge gap between the HLOQ sample and the one before. It may not be a problem with a linear response, but with a quadratic equation this gap between calibration samples does not help to fit the curve (where does it become non-linear ?) and the QCs may not help to see inaccuracies.
Personally I would set the MQC around 1/3 of the ULOQ, but I have strictly no data or rationale to defend it.
In any case there may be a need to adjust the concentration of the QC samples based on the concentrations found in the subject samples after the first few runs, if the calibration range is not optimal.
—
Regards
Ohlbe
Regards
Ohlbe
Complete thread:
- determining the QC medium haydonat 2012-10-18 00:26
- QC medium: short answer Helmut 2012-10-18 16:14
- QC medium: short answer ElMaestro 2012-10-18 16:51
- QC medium: lengthy answer Helmut 2012-10-19 15:47
- QC medium concentration in 2014 Debbie 2014-06-10 19:42
- ~geometric mean of range Helmut 2014-06-17 14:30
- ~geometric mean of range nobody 2014-06-17 14:49
- ~geometric mean of range Helmut 2014-06-17 15:11
- ~geometric mean of range nobody 2014-06-17 18:26
- ~geometric mean of range Helmut 2014-06-18 16:07
- compromise ElMaestro 2014-06-18 17:43
- compromiseOhlbe 2014-06-18 20:00
- compromise Helmut 2014-06-19 01:13
- compromise Ohlbe 2014-06-19 11:15
- compromise Helmut 2014-06-19 01:13
- compromiseOhlbe 2014-06-18 20:00
- compromise nobody 2014-06-19 14:10
- compromise ElMaestro 2014-06-18 17:43
- ~geometric mean of range Helmut 2014-06-18 16:07
- ~geometric mean of range nobody 2014-06-17 18:26
- ~geometric mean of range Helmut 2014-06-17 15:11
- ~geometric mean of range nobody 2014-06-17 14:49
- ~geometric mean of range Helmut 2014-06-17 14:30
- QC medium concentration in 2014 Debbie 2014-06-10 19:42
- QC medium: short answer Helmut 2012-10-18 16:14