Dear list members,

I am hoping to use a multilevel model to analyse data from a rater reliability study. Interest lies in assessing interrater and intrarater reliability.
I have 20 patients. Four measurements were taken in succession on each patient by each of two raters (ie: each patient was measured 8 times).
I believe this to be an example of a cross-classified MLM, with observations nested both within patients and within raters.  I am interested in fitting a model that contains an intercept, a random patient term, a rater term and a random patient by rater interaction term. The interaction term represents the interrater error, and the level 1 residual represents intrarater error. I intend to use the variance estimates from the model to calculate variance partition coefficients, representing inter and intrarater reliability. The question is whether or not I have specified the model correctly in lme4.

As I only have two raters, I understand that I should treat rater as a fixed effect.  My attempt to specify the model is as follows.

fixed<-lmer(pwv~ 1 +(1|id) + rater + (1|id:rater), data = reliability)

In fact, in a subsequent experiment, I would be interested in looking at a larger number of raters, treating rater as a random effect. Then I would specify the model as:

random<-lmer(pwv~ 1 +(1|id) + (1|rater) + (1|id:rater), data = reliability).

Would these examples specify my models of interest? As a new user of lme4 and multilevel models, I will apologise for any naivety on my part.

Many thanks,
Jack.

Jack Wilkinson, Research and Development,
Salford Royal NHS Foundation Trust, +44(0) 161 206 8125.


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