Gradient Boosting for Survival Analysis with Applications in Oncology

Cancer is one of the most deadly diseases that the world has been fighting against over decades. An enormous number of research has been conducted, via a wide scale of approaches, raging from genetic analysis to mathematical modeling. Survival analysis is a well-performed methodology frequently used to estimate the survival probability of a patient. Although there has been a large number of methods for survival analysis, efficient exploration of a high-dimensional feature space has been challenging due to its computational cost and complexity. This thesis adapts the component-wise gradient boosting algorithms for cancer survival analysis, and also proposes a new gradient boosting algorithm based on optimizing the Brier’s score. The new method is illustrated with the analysis of the microarray data of diffuse large B-cell lymphoma (DLBCL). The new gradient boosting approach not only has identified similar important biomarkers as previous statistical studies on the same data set, but also offers more insights and gained understanding on medical aspects. In addition, the performance of the new method is demonstrated through a simulation study and compared with a variety of statistical and machine learning methods