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Thread: [Clustered Robust Standard Error] - Custering

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    [Clustered Robust Standard Error] - Custering




    I am investigating the effects of age and exercise in protein expression in mouse motor neurons. I have four groups each with a limited number of measurements associated with it.
    Group (number of mice)

    Young (6)
    Young exercised (3)
    Old (6)
    Old exercised (4)

    Each mouse has measures of proteins from two different regions of the spinal cord- cervical (C) and lumbar (L). Each region has ~30 measures of protein associated with it (minimum=12). I would like to test the inter and intra region difference in protein expression between:
    1)different aged mice, i.e. Young vs. Old
    2)exercised and sedentary mice

    For example, I would like to know whether young L motor neuron has more/less protein than old L, old C, young C, young exercised L, and young exercised C. How should I cluster/group the data? Would a cluster represent L from one mouse and another cluster represent C from the same mouse? If it were done this way there would be 38 clusters in total (2 clusters from each animal) , is that a sufficient cluster number?

    Please help me as I am at my witts-end with this problem and can't seem to find many resources on this statistical technique. I apologise if my question has been too ambiguous, and will do all I can to clarify if needed. Also welcome to any suggestions for other tests if this test is unsuitable for this problem.

    Thank you kindly in advance for your expertise.

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    Re: [Clustered Robust Standard Error] - Custering


    Cross-sectional multilevel model is the concept you are looking for. This accounts for clusters at different levels and not only takes into consideration errors but also partitioned error from other levels. This later component helps reduce type 1 errors ( incorrectly rejecting null hypothesis).
    Stop cowardice, ban guns!

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