Prospective Competing Risks Analysis
Also known as: prospective CRA, prospective subdistribution hazard analysis, prospective cause-specific hazard analysis, forward-looking competing events analysis
Prospective competing risks analysis is an observational study design that follows participants forward in time from a well-defined starting point, recording all events — including those that prevent the primary event from occurring — and then estimates cause-specific incidence while correctly accounting for competing outcomes. It combines the temporal clarity of prospective cohort follow-up with the statistical rigor of competing risks methodology to avoid the overestimation inherent in standard Kaplan-Meier curves when multiple event types are present.
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When to use it
Use prospective competing risks analysis when participants face more than one mutually exclusive event type and the study follows them forward in time. It is the correct analytic approach whenever a competing event can preclude the primary outcome — common scenarios include cancer research (disease-specific vs. other-cause death), cardiovascular studies (MI vs. stroke vs. death), and transplant medicine (graft failure vs. patient death). It is particularly valuable when accurate event-cause attribution is needed, because prospective ascertainment minimizes misclassification. Do not use it when there is only one event type (use standard survival analysis), when competing events are negligible in frequency, or when the primary goal is causal inference under treatment — in that case, a randomized trial with competing risks pre-specified or a causal competing risks framework (e.g., potential outcomes) may be more appropriate.
Strengths & limitations
- Prospective follow-up provides accurate event timing and cause attribution, reducing misclassification bias common in retrospective designs.
- Correctly estimates absolute incidence of each event type without the upward bias inherent in 1 minus Kaplan-Meier estimates.
- Allows both cause-specific and subdistribution hazard modeling, enabling analysis of etiology and clinical risk prediction in the same dataset.
- Pre-specified event classification prevents post-hoc reclassification and strengthens study credibility.
- Cumulative incidence curves for all competing events sum to at most 1, providing a coherent and clinically interpretable probability framework.
- Requires long, resource-intensive prospective follow-up; impractical for rare events or diseases with very long latency unless a large cohort or registry is available.
- Loss to follow-up and censoring must be non-informative (independent of event risk) — a stronger assumption that may not hold in all clinical settings.
- Fine-Gray subdistribution hazard ratios can be difficult to interpret mechanistically because the risk set includes individuals who have already experienced a competing event.
- Requires pre-planned event adjudication; retrospective endpoint review is not equivalent and undermines the prospective design rationale.
Frequently asked
Why can I not just use Kaplan-Meier for each event type separately?
The Kaplan-Meier estimator treats all other events as independent censoring, which assumes they carry no information about the primary event probability. When competing events exist, this assumption is violated and 1 minus KM overestimates the absolute probability of the primary event. Cumulative incidence functions from competing risks analysis correctly partition total risk and always sum to no more than 1 across event types.
Should I use the Fine-Gray model or cause-specific hazard models?
The choice depends on your research question. Fine-Gray subdistribution hazard regression directly models the covariate effect on absolute cumulative incidence — it is the right choice when the goal is risk prediction or estimating the probability a patient experiences the event in practice. Cause-specific hazard models condition on being event-free and are preferred when the goal is understanding the biological mechanism generating each event type. Reporting both can provide a more complete picture.
What is the practical advantage of prospective over retrospective competing risks analysis?
Prospective follow-up ensures that event cause, timing, and clinical context are recorded at the point of occurrence, minimizing misclassification of competing events — a major source of bias in retrospective record review. It also allows pre-specified endpoint adjudication, standardized data collection procedures, and complete covariate measurement at baseline, all of which strengthen the validity of the resulting incidence estimates.
How should I handle loss to follow-up in this design?
Participants lost to follow-up are censored at their last known contact. The key assumption is that censoring is non-informative — that the probability of loss is independent of the risk of all event types. If loss is related to disease severity or treatment toxicity, the resulting estimates may be biased. Sensitivity analyses under different missing data assumptions (e.g., multiple imputation, inverse probability weighting) should be considered when loss to follow-up is substantial.
Which software packages support prospective competing risks analysis?
The R packages cmprsk (Gray's test and Fine-Gray regression), survival (cause-specific Cox models), and mstate (multi-state extensions) are the primary tools. The tidycmprsk package provides tidy-friendly wrappers. Stata's stcompet and stcrreg commands, and SAS PROC LIFETEST with the CIF option, also support competing risks estimation. All major platforms can produce cumulative incidence curves with confidence intervals.
Sources
- Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. DOI: 10.1080/01621459.1999.10474144 ↗
- Putter, H., Fiocco, M., & Geskus, R. B. (2007). Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine, 26(11), 2389–2430. DOI: 10.1002/sim.2712 ↗
How to cite this page
ScholarGate. (2026, June 3). Prospective Competing Risks Analysis. ScholarGate. https://scholargate.app/en/epidemiology/prospective-competing-risks-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Cox proportional hazardsEpidemiology↔ compare
- Kaplan-Meier AnalysisEpidemiology↔ compare
- Prospective Cohort StudyEpidemiology↔ compare
- Prospective Survival AnalysisEpidemiology↔ compare