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

*

*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?


Note that Occam's razor applies only to two pre-specified models of the world.  It does not apply when one plays with multiparameter model fits in general.
You can get odds ratios even when fits are nonlinear; you just need to specify the low and high values of X over which you want the OR.
Don't think of a spline fit as something you try temporarily just to check goodness of fit against a simple linear in X model.  This will make you ignore model uncertainty in your final model, resulting in invalid p-values and confidence intervals.  All of these issues are discussed in detail in RMS and here.
A useful overall strategy is this:

*

*when the sample size is not tiny, use spline functions for continuous predictors not already known to act linear in the log odds

*you can test for linearity of effects to impress your boss that things are not simple and to help specify models on future datasets

*keep the spline for the current dataset.  The spline function with its multiple degrees of freedom properly penalize you for not knowing an effect is linear

*use partial effects plots, interquartile-range odds ratios, and nomograms to describe the result

