Well, to me this is a new field of research and I am learning. But the first thing that came to my mind was about these special observations. Please have a look at the following table, representing concentration measurements in the same units from two different methods. they have different LOQs:
| Method A | Method B |
| 6.8 | 9.8 |
| <0.1 | <0.035 |
| 1.5 | <0.035 |
| 5.4 | 7.2 |
| 9.9 | 9.1 |
| 5 | 6 |
| 2 | 2.9 |
| 6.9 | 13.9 |
| <0.1 | <0.035 |
| <0.1 | <0.035 |
| <0.1 | 0.34 |
| <0.1 | <0.035 |
| <0.1 | <0.035 |
| <0.1 | 0.49 |
| <0.1 | <0.035 |
| <0.1 | <0.035 |
| 4 | 1.9 |
| 6.7 | 3 |
| 6.8 | 5.2 |
| 0.3 | <0.035 |
| 6.3 | 3.5 |
| 3.2 | 3 |
| >10 | >15 |
| 6.7 | 1.2 |
| 7.8 | 1.5 |
| 6.5 | 4.1 |
| 4 | 2.5 |
| 5.1 | 1.9 |
| 3.6 | 3.9 |
| 1.2 | 0.8 |
If I have to discard these observations, that will be ~30% of my dataset because that will have to be pairwise for a comparative analysis. But looking closely one can see that in most of the <LOQ observations, both methods seem to agree that there is a very low concentration. And the only observation that is above the upper LOQ, it is so for both methods. So actually this is very valuable information, qualitatively.
An initial thought can be, ok, so the observation is above 10 and 15 units respectively, so this observation has at least 10 units for one method and 15 units for the other. This is useful information, isn't it?. On the other hand, <0.035 cannot or should not be replaced by a 0.035, can it? As said before, some authors use LOQ/2 for these values, others, use 0, others discard the information.
Best,
David
