I have observations taken with different sensitivity thresholds and minimum detection levels, i.e. Lab A is less sensitive and has a minimum detection level of .2 and Lab B is more sensitive and has a minimum detection level of .02.
Edit 2: I have taken $N$ samples and have had them processed by two different labs (for stupid political reasons). Both labs send me the results and I discover that Lab A has a minimum detection level of .2 and Lab B has a minimum detection level of .02. See example:
Each row corresponds to a unique measurement taken by either lab:
Obs | Lab A | Lab B --------------------- 1 | .6 | NA 2 | 0 | NA 3 | NA | .53 4 | .2 | NA 5 | NA | .07
Edit 2: I would like to be able to use and combine results from both labs, as if they were on the same scale. The problem is that the labs used to process the samples have very different thresholds for detection and have different sensitivity levels.
I think I would like something like:
Obs | LabA | LabB | NewLab ---------------------------- 1 | .6 | NA | .64 2 | 0 | NA | .13 3 | NA | .53 | .53 4 | .2 | NA | .21 5 | NA | .07 | .07
What techniques are available to standardize the values such that there is not a large loss of information?
- Obviously, I could take the values from Lab B and replace anything less than .2 with 0 and then round them, but I want to avoid throwing away information if possible.
- One person suggested to add random noise to the values of Lab A, but I'm not sure of the benefit of this vs. simply imputing the missing values from Lab B.
Edit 1: There are no observations for which both Lab A and Lab B values are present, one will always be missing.
Edit 2: What can I do to get results from both labs on a similar scale?