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Statistical models versus machine learning for competing risks: development and validation of prognostic models.

Georgios Kantidakis1,2,3, Hein Putter4, Saskia Litière5

  • 1Mathematical Institute (MI) Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands. G.Kantidakis@lumc.nl.

BMC Medical Research Methodology
|February 24, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) models show comparable performance to statistical models (SM) for competing risks (CRs) in non-complex health data. However, SM are often better calibrated and more practical for clinical use.

Keywords:
Artificial neural networksCompeting risksPredictive performanceRandom survival forestsRegression modelsSupervised machine learningSurvival analysis

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Area of Science:

  • Health research
  • Biostatistics
  • Machine learning in healthcare

Background:

  • Chronic diseases often involve competing risks (CRs), necessitating specialized statistical models (SM) for accurate cumulative incidence estimation.
  • While machine learning (ML) is increasingly applied in clinical prediction, its use for modeling CRs in health research is less explored.
  • This study investigates the comparative performance of ML and SM for CRs in non-complex datasets.

Purpose of the Study:

  • To compare the predictive performance of machine learning (ML) techniques against traditional statistical models (SM) for competing risks (CRs).
  • To evaluate these models within a non-complex data setting, characterized by small to medium sample sizes and low dimensionality.
  • To assess the practical implications and calibration accuracy of ML versus SM for CRs.

Main Methods:

  • A dataset of 3826 patients with extremity soft-tissue sarcoma (eSTS) was utilized, with disease progression as the event of interest and death as the competing event.
  • Two SM (cause-specific Cox, Fine-Gray) and three ML techniques (PLANNCR original, PLANNCR extended, RSFCR) were compared for CRs.
  • Model performance was evaluated using Brier score and Area Under the Curve (AUC) at 2, 5, and 10 years, with a focus on discrimination and calibration.

Main Results:

  • ML models achieved performance comparable to SM in terms of Brier score and AUC for CRs at 2, 5, and 10 years.
  • Confidence intervals for performance metrics between ML and SM overlapped, indicating similar predictive discrimination.
  • Statistical models demonstrated superior calibration compared to ML models in this non-complex data setting.

Conclusions:

  • For non-complex survival data, ML techniques may offer comparable predictive performance but require significant implementation time and resources.
  • Statistical models are often more practical and better calibrated for CRs in simple clinical settings.
  • ML should be considered complementary to SM as exploratory tools, with a critical need for improved model calibration.