Competing Risk Analysis in HCT & IEC Research : Transplant Cell Ther | May 2026

Introduction

Competing risks are frequently encountered in hematopoietic cell transplantation (HCT) and immune effector cell (IEC) therapy research, particularly when mutually exclusive outcomes coexist. A classical example is treatment-related mortality (TRM) competing with disease relapse, where early non-relapse death precludes the possibility of relapse occurrence. Mismanagement of competing risks can produce biased survival estimates and misleading hazard interpretations, making proper statistical handling essential in transplantation and cellular therapy studies.

Problem Statement

Competing risks are commonly analyzed incorrectly using standard Kaplan–Meier (KM) methods that censor competing events, violating the assumption of non-informative censoring. This leads to systematic overestimation of event probabilities. In addition, Fine–Gray subdistribution hazard models are frequently misinterpreted as causal models despite their prognostic—not etiologic—nature. Many clinicians remain unfamiliar with the conceptual distinctions between cumulative incidence, cause-specific hazards and subdistribution hazards, limiting the quality and interpretability of transplant outcome research.

Summary

This practical review provides a clinician-oriented primer on competing risk methodology with a detailed stepwise guide for implementation using the open-access R and RStudio statistical environment. The authors first explain the limitations of naïve Kaplan–Meier analysis in competing-risk settings. When competing events are censored using standard KM methods, the resulting 1-KM estimator inflates the true probability of the event of interest because it ignores the dependency between mutually exclusive outcomes.

The review emphasizes the importance of the cumulative incidence function (CIF), which appropriately accounts for the probability of remaining free from competing events. Unlike KM estimation, CIF conditions the risk of the primary event on the absence of competing outcomes over time, generating more accurate cumulative risk estimates. Gray’s test is highlighted as the preferred statistical method for comparing CIF curves between groups.

The manuscript also clarifies the distinction between cause-specific hazard ratios (HRcs) and subdistribution hazard ratios (HRsd). Cause-specific Cox proportional hazard models censor competing events and estimate instantaneous event rates among individuals still at risk, making them more suitable for mechanistic or etiologic inference. In contrast, Fine–Gray models retain competing events in the risk set and directly model cumulative incidence, making HRsd more useful for patient-level prognostic prediction rather than causal interpretation.

A key conceptual message is that Fine–Gray models may generate misleading associations when variables strongly influence competing events. For example, a covariate associated with higher TRM may appear to reduce relapse incidence simply because patients die before relapse can occur. The authors demonstrate through simulations that HRsd estimates become increasingly biased as the association with competing events strengthens, reinforcing the need for cautious interpretation of Fine–Gray regression outputs.

The second half of the review provides a practical tutorial for conducting competing risk analyses in R. Using a real-world allogeneic HCT dataset in acute myeloid leukemia, the authors illustrate cumulative incidence estimation, visualization, Gray’s testing, cause-specific Cox regression and Fine–Gray regression modeling. Multiple R packages are introduced, including tidycmprsk, survival, ggsurvfit, and gtsummary, enabling generation of publication-ready cumulative incidence plots and regression tables.

The review concludes by emphasizing that Fine–Gray models are best suited for prognostic estimation, whereas cause-specific Cox models are preferable for etiologic or mechanistic research questions. The authors strongly advocate collaboration with trained biostatisticians to minimize methodological bias and improve rigor in transplantation and cellular therapy research involving competing risks.