Competing Risks Analysis in Survival Data
Summary
Competing risks analysis addresses situations in which participants under observation may experience one of several mutually exclusive events, each of which precludes the occurrence of the others. Traditional survival methods, such as the Kaplan–Meier estimator and standard Cox proportional hazards model, can yield biased measures of absolute or relative event probabilities when a competing event is treated as simple censoring. Modern approaches distinguish between cause-specific hazards, which quantify the instantaneous risk of each event type, and subdistribution hazards, which directly relate covariates to the cumulative incidence of an event in the presence of competing events. The cumulative incidence function (CIF) provides a direct estimate of the probability of an event over time, accounting for competing events. These methods underpin clinical trial design, epidemiological registry analyses and risk prediction in fields as diverse as oncology, cardiovascular science and orthopaedics. Recent methodological advances have improved flexibility through smooth hazard estimation, better handling of non-proportional hazards and time-dependent covariates, and the application of simulation frameworks for study planning. Practitioners now routinely employ multi-state modelling, flexible parametric forms and robust software implementations to yield more accurate absolute and relative risk estimates, guiding both policy decisions and individualized patient counselling.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Competing Risks Analysis in Survival Data publication trend
The graph below shows the total number of articles in competing risks analysis in survival data across all publications each year (not limited to Nature Index journals).
Technical terms
Competing risk: An event type that precludes the occurrence or alters the probability of the primary event of interest.
Cumulative incidence function (CIF): The probability of experiencing a specific event by a given time, accounting for competing events.
Cause-specific hazard: The instantaneous rate at which a particular event occurs, assuming no prior occurrence of any event.
Subdistribution hazard: A hazard function that relates covariates directly to the CIF by retaining subjects in the risk set after competing events.
Non-proportional hazards: A situation in which the effect of a covariate on the hazard varies over time rather than remaining constant.
References
- Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions. BMC Medical Research Methodology (2013).
- Understanding competing risks: a simulation point of view. BMC Medical Research Methodology (2011).
- Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes. BMC Medical Research Methodology (2018).
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.