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i have ultrasound data measuring strain of myocardium as the dependant variable. Now i want to compare reliability and agreement of the measurement system between the 2 methods, the test retest and the difference between observers. i am using a LMM to calculate repeatability coeff (2.77 * within patient SD) and ICC (within pat SD / (within pat SD + between pat SD).

I have several factors : patient (1-40), observors (1,2), Method (1,2), scanner (1,2). I am not completely sure how to model this, i know from what i read that the patient should be the random clustering variable, but if seems if i am to calculate ICC and RC's above i have to have observer as a random factor to proportion the variance. does anyone have experience with this type of model? enter image description here

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    $\begingroup$ It sounds like you might want crossed random effects, for patient and observer. Does this this help ? $\endgroup$
    – Robert Long
    Commented Oct 6, 2020 at 8:57
  • $\begingroup$ thanks for the answer btw! Yeah that is what i was thinking as well, but i started trialling a few things and the best model i come up with is all random factors , and just a fixed intercept. is this even possible? $\endgroup$
    – K S
    Commented Oct 6, 2020 at 10:36
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    $\begingroup$ No problem. It wasn't an answer, just a comment, as there isn't enough information about your data yet. When fitting random intercepts you need to ensure there are enough levels of each factor (6 as a minimum, is a good rule of thumb). Perhaps you can include more info about your study and data (e.g. by including the output of str(data)).What do you mean "all random factors" and what do you mean by "best model" ? $\endgroup$
    – Robert Long
    Commented Oct 6, 2020 at 10:42
  • $\begingroup$ I am using jamovi btw which is lme4 based. So i mean the largest r2 and the lowest AIC/BIC to indicate model fit, but im not sure if this is appropriate either. so i have the patient as a random factor/ grouping variable, plus the observer and scanner also as random effects. this is based on 2 separate models for each method. so my longitudinal data headers are : Patient,Scanner, Obs, GLS AFI,GLS 2DS. $\endgroup$
    – K S
    Commented Oct 6, 2020 at 11:21
  • $\begingroup$ OK but how many levels of observer and scanner do you have ? $\endgroup$
    – Robert Long
    Commented Oct 6, 2020 at 11:23

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