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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