Logical analysis of survival data: prognostic survival models by detecting high-degree interactions in right-censored data.

Abstract

Survival analysis involves predicting the time to event for patients in a dataset, based on a set of recorded attributes. In this study we focus on right-censored survival problems. Detecting high-degree interactions for the estimation of survival probability is a challenging problem in survival analysis from the statistical perspective. We propose a new methodology, Logical Analysis of Survival Data (LASD), to identify interactions between variables (survival patterns) without any prior hypotheses. Using these set of patterns, we predict survival distributions for each observation. To evaluate LASD we select two publicly available datasets: a lung adenocarcinoma dataset (gene-expression pro.les) and the other a breast cancer dataset (clinical pro.les). The performance of LASD when compared with survival decision trees improves the cross-validation accuracy by 18% for the gene-expression dataset, and by 2% for the clinical dataset. Executable codes will be provided upon request.