OK, I am working on a corrected LASSO logistic model, which addresses the model building dependence (i.e., variables were not declared a priori but established via the modeling process) and all candidate covariates were standardized to unify their scales before entering them into the model.

But for this question, I believe we can just call this a logistic regression question. So I standardize all candidate variables entered into the model (e.g., 3 are continuous and the rest ~ 10 are binary). We are ignoring the continuous variables going forward in this post, since their interpretation is straightforward. Now the binary variables during the standardization process likely got their value of either 1 or 0 subtracted by the mean (prevalence), then divided by a standard deviation feature. So an example standardized binary variable ends up taking the following values: -3.10 or 0.32, while another binary variable is: -0.72 or 1.38.

So, a complaint about possibly doing this, is that standardized binary variables are much more difficult to interpret. Though, it was felt necessary to unify candidate variable scales given the modeling approach.

I will happily entertain any feedback or comments - Thanks!

But for this question, I believe we can just call this a logistic regression question. So I standardize all candidate variables entered into the model (e.g., 3 are continuous and the rest ~ 10 are binary). We are ignoring the continuous variables going forward in this post, since their interpretation is straightforward. Now the binary variables during the standardization process likely got their value of either 1 or 0 subtracted by the mean (prevalence), then divided by a standard deviation feature. So an example standardized binary variable ends up taking the following values: -3.10 or 0.32, while another binary variable is: -0.72 or 1.38.

So, a complaint about possibly doing this, is that standardized binary variables are much more difficult to interpret. Though, it was felt necessary to unify candidate variable scales given the modeling approach.

**So my question/comment: when interpreting the outputted log odds converted to odds ratios, am I now just saying the odds of the outcome are XX times greater for a 1 standard deviation increase in the prevalence of the binary variable?**I will happily entertain any feedback or comments - Thanks!

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