Analysis of sarcoma and non-sarcoma clinical data with statistical methods and machine learning techniques

Abstract

This thesis sprang from an interdisciplinary collaboration between the European Organisation for Research and Treatment of Cancer (EORTC), the Mathematical Institute of Leiden University, and the Leiden University Medical Center (LUMC) Department of Medical Oncology. Research was split into two separate parts. In Part I, the main goal was to provide modern efficacy thresholds for designing new phase II clinical trials for common histotypes of locally advanced or metastatic soft-tissue sarcoma patients. An update was necessary as well-established values were reported back in 2002 by the EORTC – Soft Tissue and Bone Sarcoma Group.

Nowadays, there is a growing interest by the medical community in applying machine learning to predict clinical outcomes. In Part II, the main goal was to investigate the potential of existing and novel machine learning techniques compared with traditional statistical benchmarks for real-life clinical data (small/medium or large sample sizes, low- or high-dimensional settings) with time-to-event endpoints. Findings indicate an urgent need to pay closer attention to calibration (absolute predictive accuracy) of machine learning techniques to achieve a complete comparison with statistical models.