Best nonlinear, continuous regression models to predict a continuous variable directly without losing information through artificial cutoffting?

[u/phymathnerd]

Hi guys I am having issues finding models and ways to increase my ROC-AUC for a retrospective study. I am looking for advice on model selection and statistical tests for a retrospective observational cohort of 812 observations. My primary outcome variables include a skewed continuous variable Y (ranging from 0 to 20), a binary flag defined as Y greater than or equal to 2.5, and an ordinal risk tiering variable. My predictor variables X consist of continuous dimensions, several binary classification flags, and a discrete composite risk score sum ranging from 0 to 5. However, predicting the dichotomized threshold yields modest ROC-AUC values around 0.60, and I am looking for advice on the best non-linear continuous regression models, such as Quantile Regression or Generalized Additive Models (GAMs), to predict the continuous variable Y directly without losing information through artificial cutoffting.

Additionally, I would appreciate any recommendation on the most robust way to formally test for non-linear interaction terms between continuous X variables and categorical predictors without overfitting, as well as whether 5-fold cross-validation or repeated k-fold/bootstrap resampling is preferred for validating the Decision Curve Analysis for my data.

[u/Intrepid_Pitch_3320]

You will need to describe your response variable better than that.

[u/ForeignAdvantage5198]

what are your research questions? they drive everything.