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    Using logistic regression to compute age-adjusted rates




    Dear All,

    Would anybody please give me a breakdown no how to compute "age-adjusted proportions" based on the logistic regression coefficients?

    That is specifically, I have CVD status(1 or 0), obesity status (1 or 0), and age in the following model:

    (*) g(E CVD_i) = beta_0 + beta_1 * obese_i + beta_2 * age_i,

    where g(x)=log(x/(1-x)).

    So, what do I do after I have computed betas? (And why?)

    TIA for any hints
    Rkk

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    Re: Using logistic regression to compute age-adjusted rates

    What estimate do you want in particular? Typically you want to exponentiate the coefficient.
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    Re: Using logistic regression to compute age-adjusted rates

    Dear Hlsmith,

    Thank you for your reply! I would like to compute age-adjusted estimates of proportion of CVD-positive subjects in two groups: obese and non-obese. Since I am not sure what they mean by this, hence my question: what exatctly it is?

    When you say "to exponentiate the coefficient", would you be very spicific, please?

    Thank you! - R

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    Re: Using logistic regression to compute age-adjusted rates

    Given the information you provided and the output of a basic logistic regression model:


    -all you should have to do is exponentiate the coefficient for obesity to get the odds ratio of obese status compared to a non-obese status for CVD, while controlling for age. You would only have one estimate, since obesity is a binary variable and one group has to be the reference group.


    Not sure if you are looking for something else or not, difficult to tell given you description.
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    Re: Using logistic regression to compute age-adjusted rates


    Dear Hlsmith,

    Thank you very much, it does make sense to me and it is really very helpful! At the same time I was, in fact, looking to compute adjusted proportion estimate (not the odds ratio). Based on odds ratio we cannot compute the proportions, can we?

    Example: in Canadian Inst. for Health Informatics they fit a logistic regression then modify the unadjusted QI results as if each facility served a standard reference population, that is replacing the values of a covariate on the right-hand side of the logistic regression model with an average, as in their publication "What Does 'Adjusted' Mean? A Demonstration of Quality Indicator Calculation in Nursing Homes".

    In my example this might mean replacing age on the right-hand-side with an average age over the entire dataset. Yet another source suggested weighted averages. How would you comment on this?

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