Adaptive Competing Risks Analysis
Also known as: adaptive Fine-Gray analysis, group-sequential competing risks, adaptive subdistribution hazard analysis, competing risks adaptive design
Adaptive competing risks analysis combines the Fine-Gray subdistribution hazard framework — which models the cumulative incidence of one cause of failure in the presence of other mutually exclusive causes — with adaptive or group-sequential interim monitoring rules. This allows a clinical trial or observational study to be modified mid-course (e.g., sample size reassessment, early stopping) based on accumulating competing-risk data while maintaining pre-specified type I error control.
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When to use it
Use adaptive competing risks analysis when (1) the primary outcome is a time-to-event endpoint subject to mutually exclusive competing events (e.g., disease-specific death alongside all-cause mortality or non-fatal events), (2) uncertainty at the design stage makes mid-course adaptations valuable (e.g., uncertain event rates, effect size, or accrual), and (3) the study is large enough — typically a multi-site randomized trial or registry study — to justify the infrastructure for interim monitoring. Do NOT use when (a) competing events are negligible or all-cause mortality is the only endpoint (standard adaptive survival analysis suffices), (b) the sample is too small for interim monitoring to yield meaningful conditional power estimates, or (c) the adaptive rules cannot be pre-specified and locked before unblinding, which would invalidate type I error control.
Strengths & limitations
- Correctly models the probability of failure from a specific cause without the bias introduced by treating competing events as independent censoring.
- The adaptive layer allows sample size reassessment or early stopping, improving efficiency and reducing exposure of participants to an inferior treatment.
- Maintains strict type I error control through pre-specified alpha-spending functions even when the study design is modified mid-course.
- Produces clinically interpretable outputs — cumulative incidence functions — that directly inform treatment decisions better than cause-specific hazard ratios alone.
- Applicable to both randomized clinical trials and large observational registry studies with event-time data.
- The subdistribution hazard ratio from the Fine-Gray model does not have as direct a biological interpretation as the cause-specific hazard ratio; it mixes the effects of covariates on occurrence and on competing event survival.
- Requires pre-specification of all adaptive rules, alpha-spending functions, and interim look schedules before data unblinding — post-hoc adaptations invalidate inference.
- Interim data review by an unblinded statistician or independent data monitoring committee adds logistical and regulatory complexity.
- Small to moderate sample sizes may not provide sufficient conditional power at interim looks to make adaptation decisions reliable.
Frequently asked
What is the difference between the Fine-Gray model and a cause-specific hazard model?
The cause-specific hazard model estimates the instantaneous rate of failure from a specific cause among those still event-free — competing events are treated as censored. The Fine-Gray subdistribution hazard model keeps subjects who experience a competing event in the risk set (with decreasing weight), so it directly models the cumulative incidence function. For predicting absolute risk in a population, the Fine-Gray CIF is more appropriate; for understanding biological mechanisms of a single failure type, cause-specific hazards may be preferable.
How do I choose the alpha-spending function for adaptive interim monitoring?
The O'Brien-Fleming spending function is conservative early and spends more alpha late — suitable when early stopping for efficacy is unlikely. The Pocock spending function allocates alpha more evenly, making early stopping for efficacy easier but requiring a lower final threshold. The choice should be pre-specified in the protocol and statistical analysis plan; regulatory agencies (FDA, EMA) require this pre-specification before any unblinded interim analysis.
Can I add covariates to the Fine-Gray model in an adaptive design?
Yes. Covariate adjustment in the Fine-Gray model is common for baseline imbalances, prognostic stratification factors, or increasing precision. In an adaptive design, the covariates included in the model should be pre-specified; adding or removing covariates after viewing interim data requires a pre-planned covariate-selection rule or risks inflating the type I error.
Is adaptive competing risks analysis accepted by regulatory agencies?
Adaptive trial designs more broadly are accepted by the FDA (see their 2019 guidance on adaptive designs for clinical trials) and EMA, provided the adaptive rules, alpha-spending functions, and operating characteristics are pre-specified and the trial maintains blinding during interim reviews by an independent data monitoring committee. Competing risks endpoints are also standard in oncology and cardiovascular submissions, though the analysis plan should clarify whether the primary endpoint is the CIF or the cause-specific hazard.
What software supports adaptive competing risks analysis?
The R package cmprsk implements the Fine-Gray model and Gray's test. The mstate package supports multistate extensions. For adaptive design operating characteristics, gsDesign and rpact support group-sequential frameworks; combining these with competing risks models typically requires custom simulation code or purpose-built packages such as PASS (NCSS) for sample size calculations under competing risks.
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 ↗
- Beyersmann, J., Allignol, A., & Schumacher, M. (2012). Competing Risks and Multistate Models with R. Springer. ISBN: 978-1461420767
How to cite this page
ScholarGate. (2026, June 3). Adaptive Competing Risks Analysis. ScholarGate. https://scholargate.app/en/epidemiology/adaptive-competing-risks-analysis
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