- #1
lavoisier
- 177
- 24
Hi everyone,
I'm trying to find a test to compare two assays, and I'm not sure which one I should use. Could you please help?
Here's the situation.
My company is setting up two assays, A and B.
Both assays are supposed to measure the same property of certain items, i.e. we would expect A and B to give broadly the same value of the property when applied to the same item.
We plan to test N=100000 items.
However, we can only run either A or B on all of these items, because running both is too expensive.
A is considered less accurate, but is also less expensive than B, so if there is enough 'agreement' between A and B, we would prefer to run A.
We are therefore trying to measure if and to what extent A and B are 'agreeing'.
The plan is to select a random subset of the 100000 items, say 5000, run both A and B on them and compare the results, in particular looking at whether the same items did indeed give broadly the same assay result in both assays.
How would you analyse this?
I was thinking of using the concordance correlation coefficient (CCC), or maybe the rank correlation coefficient.
Is this appropriate?
And if so, can a significance be calculated for these coefficients, like there is one for the 'standard' linear correlation coefficient?
My other question is: are 5000 items out of 100000 sufficient to give us enough confidence in the 'agreement' that we observe?
E.g. how would we calculate the minimal number n of items to pre-test in both assays to reach a significance p<0.05? But in fact, isn't the significance related to the actual coefficient, which one doesn't know before, so how is it even possible to estimate n?
Thank you!
L
I'm trying to find a test to compare two assays, and I'm not sure which one I should use. Could you please help?
Here's the situation.
My company is setting up two assays, A and B.
Both assays are supposed to measure the same property of certain items, i.e. we would expect A and B to give broadly the same value of the property when applied to the same item.
We plan to test N=100000 items.
However, we can only run either A or B on all of these items, because running both is too expensive.
A is considered less accurate, but is also less expensive than B, so if there is enough 'agreement' between A and B, we would prefer to run A.
We are therefore trying to measure if and to what extent A and B are 'agreeing'.
The plan is to select a random subset of the 100000 items, say 5000, run both A and B on them and compare the results, in particular looking at whether the same items did indeed give broadly the same assay result in both assays.
How would you analyse this?
I was thinking of using the concordance correlation coefficient (CCC), or maybe the rank correlation coefficient.
Is this appropriate?
And if so, can a significance be calculated for these coefficients, like there is one for the 'standard' linear correlation coefficient?
My other question is: are 5000 items out of 100000 sufficient to give us enough confidence in the 'agreement' that we observe?
E.g. how would we calculate the minimal number n of items to pre-test in both assays to reach a significance p<0.05? But in fact, isn't the significance related to the actual coefficient, which one doesn't know before, so how is it even possible to estimate n?
Thank you!
L