Spline-transform regression - concept clarification I am learning spline transformation and am confused about several concepts. Any guidance is all appreciated!

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*Am I understanding this correctly: I should only spline-transform my continuous predictors if my logistic regression model does not have a good model fit?

*It appears that we cannot obtain the odds ratio from the spline-transformed logistic regression, is this correct? If so, how should I report the finding (as I always think odds ratio is the must-report outcomes in logistic regression)?

*Is it allowed to log transform some predictors and spline transform the others in my regression model or I 'MUST' keep their methods of transformation consistent?


Interesting questions!

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*In general, if you have a simple method like a log transformation that does the same job comparably well as the more complicated method (spline transformation) stick to the simpler method. Think of Occam's razor. How do you evaluate "comparably well"? Use methods for model evaluation like information criteria, cross-validation etc.

*You still have an odds ratio, but the nonlinear effect of your covariate is not interpretable anymore because its effect on the odds ratio is not constant anymore. I think one way of reporting it, would be to just plot the nonlinear effect on the scale of the (log) odds ratio. I would argue that this is rather uncommon in the (applied) literature.

*It is perfectly fine to use different methods of transformation in the same model.

