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


RESULTS
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.


BACKGROUND
Executable codes will be provided upon request.
Kronek
Louis-Philippe
LP
G-SCOP, Grenoble-Science Conception Organization and Production, Grenoble, France. louis-philippe.kronek@g-scop.fr
Reddy
Anupama
A
D015982:Bias; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016015:Logistic Models; D011379:Prognosis; D016016:Proportional Hazards Models; D016019:Survival Analysis; D015996:Survival Rate