Re: Inter-instrument agreement

AMY <andrea.meyer@xxxxxxxxx> wrote in news:8f4c0fee-9b0c-4f56-82bb-
800413c4db96@xxxxxxxxxxxxxxxxxxxxxxxxxxx:

Inter-instrument agreement is often measured using the intra class
correlation coefficient (ICC). Here, I want to know whether the
agreement between two instruments (or methods), let's call them y1 and
y2, varies with a third variable (=x) that is continuously
distributed. As an example one might be interested in knowing whether
the agreement between the self-reported number of binges in a given
time interval and the corresponding number using ecological momentary
assessment (EMA) is negatively related to the subject's BMI. If the
covariate was categorical, I could simply compute the ICC for each
category separately. In the case of a continuous covariate this is
obviously not possible unless I categorize the covariate beforehand
(at the cost of reduced power). My idea is now to compute a kind of
index, such as the difference between y2 and y1 and regress it against
x using the formula y2-y1=a+b*x. This is the same as using a multiple
regression model in which the coefficient of y1 is forced to be one:
y2=a+b*x+1*y1. Another, and maybe more useful model because it more
directly addresses the question at hand, would be: y2=a+b*x
+c*y1+d*x*y1. In this model one would primarily be interested in the
interaction coefficient d which tests whether the relationship between
y2 and y1 is moderated by x.

As I am unsure about how to proceed in this matter I would be grateful
to any comments regarding these thoughts, including references that
help me getting on.

Many thanks in advance

AMY


Altman DG, Bland JM. (1983) Measurement in Medicine: the Analysis of
Method Comparison Studies. The Statistician 32, 307-317,

Bland JM, Altman DG. (1986). Statistical methods for assessing agreement
between two methods of clinical measurement. Lancet i, 307-310,

Bland JM, Altman DG. (1995) Comparing methods of measurement: why
plotting difference against standard method is misleading. Lancet
--
Scott
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