Survival Analysis and Time-to-Event Modeling
Summary
Survival analysis encompasses a suite of statistical methods for analysing the time until an event of interest occurs, often under incomplete observation due to censoring. Core concepts include the survival function, which describes the probability of surviving beyond a given time, and the hazard function, which represents the instantaneous risk of event occurrence. Non-parametric techniques such as the Kaplan–Meier estimator and log-rank tests allow for the estimation and comparison of survival curves without assuming an underlying distribution. Semi-parametric approaches, most notably the Cox proportional hazards model, quantify the effect of covariates on hazard rates without specifying the baseline hazard. Parametric models and accelerated failure time formulations offer an alternative when particular distributional forms can be justified. Extensions to competing risks, multi-state frameworks and models with time-varying covariates address more complex event structures, while frailty models incorporate unobserved heterogeneity. These methods are widely applied in medicine, engineering, ecology and economics, informing prognosis, policy decisions and the design of clinical trials across global contexts.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Survival Analysis and Time-to-Event Modeling publication trend
The graph below shows the total number of articles in survival analysis and time-to-event modeling across all publications each year (not limited to Nature Index journals).
Technical terms
Censoring: A condition in which the exact time of event occurrence is unknown but is constrained to lie within a given interval or beyond a threshold.
Survival function: A function representing the probability that an individual or unit survives beyond a specified time.
Hazard function: The instantaneous rate at which events occur at time t, conditional on survival up to that time.
Kaplan–Meier estimator: A non-parametric estimator of the survival function that accounts for censored observations.
Cox proportional hazards model: A semi-parametric regression model relating covariates to hazard rates without specifying the baseline hazard form.
Competing risks: A modelling framework for scenarios in which multiple distinct event types may preclude the occurrence or observation of the primary event of interest.
References
- Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment. British Journal of Cancer (2023).
- How long do transport infrastructure last: evidences from Norwegian roads and rail network. European Transport Research Review (2024).
- Improving efficiency of fitting Cox proportional hazards models for time-to-event outcomes in genome-wide association studies (GWAS). Bioinformatics Advances (2023).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.