Modeling strategies for a flexible estimation of the crude cumulative incidence in the context of long follow-ups: model choice and predictive ability evaluation

The proposed statistical learning workflow, informed by domain expertise, allows for incorporating clinically relevant complexities in the prognostic modeling of distant recurrences in breast cancer. Our results suggest that, in many cases, a nuanced and flexible model structure may better serve future predictions than simpler models. This approach underscores the value of balancing model simplicity and complexity to achieve meaningful, clinically useful insights.