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Thread: two continuous & two categorical variables: how to compare

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    two continuous & two categorical variables: how to compare




    Dear all,

    I have the following biological data:
    • 1st variable (continuous, normally distributed): concentration of a hormone in blood
    • 2nd variable (continuous): age of donors
    • 3rd variable (categorical): genotype (control vs mutation-carrier)
    • 4th variable (categorical): gender (male vs female)

    I want to test whether there is a difference in the hormone levels between control donors and mutation carriers, but age and gender might bias the direct comparison using a t-test. For instance, I see that age affects the hormone levels in the control group, but not in the mutation group.

    What sort of normalization/correction test shall I apply prior to comparing horomonal levels between the control and mutation groups?

    Thanks a lot for helping!

    Best wishes,
    Alexei

    University of Luebeck, Germany

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    Re: two continuous & two categorical variables: how to compare

    hi,
    I think the best would be to simply build a regression model with one continuous and two discrete variables with interactions amongst them. That will give you all the detailed infos you need.
    regards

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    Alexei (08-02-2016)

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    Re: two continuous & two categorical variables: how to compare

    Thanks for your suggestion, rogojel!

    What I've learned so far is that I can use the analysis of covariance (age as a covariant) with two categorical variables. However, it assumes that my continuous data can be modelled with a linear regression. But, as fas as I understood, I should first test whether linear regression is applicable for my data by analyzing residual plots. And I should test for potential outliers (measuring Mahalanobis’ distance or Cook’s D).
    Am I thinking in the right direction?
    Is there anything I'm still missing here?

    Thank you!

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    Re: two continuous & two categorical variables: how to compare


    yes,
    that makes sense. In order to have residuals you should first build the regression model - same for Cooks distance.
    Regatds

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