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arXiv Statistics ML By Lasai Barre\~nada, Andrew Vickers, Laure Wynants, Bieke Tack, Dirk Timmerman, Ewout Steyerberg, Ben Van Calster

Cost-based variable selection for prediction models

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The article introduces a decision‑analytic variable selection algorithm that optimizes clinical prediction models by balancing predictive performance against the costs and harms of measuring variables. It exhaustively compares net benefit across all predictor combinations, with a backward‑elimination extension for high‑dimensional data, and selects variables only when their added predictive value outweighs their measurement costs. The method, implemented in the R package NBvarsel, was demonstrated on simulated data and three real case studies, producing parsimonious models that maintained discrimination and calibration while reducing unnecessary clinical burden.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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