What are the advantages of using a Machine Learning (NN) method instead of regression model in survival analysis? Suppose that I have a sample of survival times $t_1,...,t_n$, censoring indicators $d_i = I(t_i < C)$, and covariates $x_i\in{\mathbb R}^P$. Suppose that

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*I have a flexible parametric regression model based on splines, including time dependent effects, and all that show.


*I can train a neural network based on recent developments on the literature.
Assuming that the computational cost of both approaches is the same.
Question: What advantages learning a Neural Network would bring compared to the flexible parametric regression model?

The neural net will pick up also interaction effects, unlike your Cox model with (one-dimensional) splines. However, you will need a very large sample size to beat a well built additive Cox model in terms of predictive performance. Furthermore, working with neural nets requires a lot of practical experience (so many things can go wrong) and a bullet proof validation strategy.
If you really want to sacrifice additivity, then I'd suggest to start with a survival random forest. There, you will also have to work with a clean validation strategy, but it is much easier.
