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Thread: Compensate for correlated observations

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    Compensate for correlated observations




    Dear users
    I have an issue that I am not to solve alone and I am here to ask for your help. I work in the ophthalmology field and I have a group of patient included in a clinical study and the goal of this study is to correlate some continous parametric variables and 1 nonparametric variable each other. However, half of the patients had included one eye only, while the others both eyes, which are correlated. How can I compensate this bias?
    Thanks in advance

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    Re: Compensate for correlated observations

    You need to run a multilevel model that controls for this covariance. You will see if you look for it, this is what is used in many ophthalmology papers. I have helped on a coupe and it is what I did. They may use other terms like patient clusters or hierarchical model, but all get at controlling for this dependency.
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    Re: Compensate for correlated observations

    May I use a GEE were i put eye as a within variable?

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    Re: Compensate for correlated observations

    GEE's are probably the best choice when correlation structures are not exactly known. However, in your case, you have a clear nested design where eyes are nested within individuals. Thus, you should use a repeated-mesaures ANOVA or a (G)LMM, where the patient ID is your random coefficient.

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    Re: Compensate for correlated observations


    Agreed. Though, many people may say GLMM trump repeated-measure ANOVA, in that it has a lot of options, but most importantly it doesn't drop observations when data are missing.
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