2024/03/12 by Camila Fernández, Fernandez, Camila, Chung Shue Chen +4 · 1 citation
Computer Science · Decision Sciences · Psychology · #FOS: Computer and information sciences #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Mental Health Research Topics #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2403.07460
openalex created_date 2024/03/12 · openalex publication_date 2024/03/12 · openalex updated_date 2026/07/28
Time-to-event analysis is a branch of statistics that has increased in popularity during the last decades due to its many application fields, such as predictive maintenance, customer churn prediction and population lifetime estimation. In this paper, we review and compare the performance of several prediction models for time-to-event analysis. These consist of semi-parametric and parametric statistical models, in addition to machine learning approaches. Our study is carried out on three datasets and evaluated in two different scores (the integrated Brier score and concordance index). Moreover, we show how ensemble methods, which surprisingly have not yet been much studied in time-to-event analysis, can improve the prediction accuracy and enhance the robustness of the prediction performance. We conclude the analysis with a simulation experiment in which we evaluate the factors influencing the performance ranking of the methods using both scores.