Manuscript Topics

Survival analysis is a branch of statistics for analyzing the expected duration of time until one event occurs. This issue aims to compile high-quality original research and review articles that develop and evaluate cutting-edge statistical techniques to model time-to-event data with applications in medicine, epidemiology, public health, biology, economics, and other fields.

Potential topics include but are not limited to: novel parametric/semi-parametric/nonparametric survival models; machine learning for survival analysis; causal inference methods; competing risks and multi-state models; joint models for longitudinal and survival data; excess hazard models; analysis of clustered survival data; predictive models; survival analysis in the era of big data; precision medicine applications; and innovative software tools. Both methodological contributions and applied work demonstrating substantial improvements over existing methods are welcome.

To highlight recent progress, we are particularly interested in work developed within the last 10 years. All submissions will undergo rigorous peer review. This special issue provides an ideal forum to showcase high-caliber, state-of-the-art statistical methods advancing the field of survival analysis.

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