Machine Learning versus Cox Models for Predicting Overall Survival in Patients with Osteosarcoma: A Retrospective Analysis of the EURAMOS-1 Clinical Trial Data

Cancers, vol. 16, pp. 2880

Abstract

Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall survival (OS) using osteosarcoma data from the EURAMOS-1 clinical trial (NCT00134030). The well-established Cox proportional hazard model is compared to the extended Cox model that includes time-varying effects, and to the ML methods random survival forests and survival neural networks. The impact of eight variables on OS predictions is explored. Results are compared on different model performance metrics, variable importance, and patient-specific predictions. The article provides comprehensive insights to aid healthcare researchers in evaluating diverse survival prediction models for low-dimensional clinical data.

Authors 5

1. Marta Spreafico corresponding Aachen Department of Biomedical Data Sciences—Medical Statistics Mathematical Institute

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences—Medical Statistics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands

   Mathematical Institute, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands

2. Audinga‐Dea Hazewinkel

   London School of Hygiene & Tropical Medicine

   Affiliation as printed

   Department of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK

3. Michiel A. J. van de Sande Aachen Department of Orthopedic Surgery

   Leiden University Medical Center · Princess Máxima Center

   Affiliation as printed

   Department of Orthopedic Surgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands

   Department of Orthopedic Surgery, Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS Utrecht, The Netherlands

4. Hans A. J. Gelderblom Aachen Department of Medical Oncology

   Leiden University Medical Center

   Affiliation as printed

   Department of Medical Oncology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands

5. Marta F. Fiocco Aachen Department of Biomedical Data Sciences—Medical Statistics Mathematical Institute

   Leiden University · Leiden University Medical Center · Princess Máxima Center

   Affiliation as printed

   Department of Biomedical Data Sciences—Medical Statistics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands

   Mathematical Institute, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands

   Trial and Data Center, Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS Utrecht, The Netherlands