Spline-Based Joint Modeling of PFS and Cumulative Incidence with Interval and Right Censoring

Thomas M. Menino Convention & Exhibition Center 

Progression-free survival (PFS) is a common endpoint in oncology trials, yet its estimation becomes challenging when progression is interval-censored and death is right-censored. Standard approaches, such as the Aalen–Johansen estimator fail to respect the clinical ordering constraint that progression must occur prior to death. We propose a nonparametric maximum likelihood estimator (NPMLE) for the joint cumulative distribution function of progression and death, employing a sieve approach with I- and M-splines to ensure monotonicity and clinical interpretability. The proposed method is fit via convex optimization and accommodates both right- and interval-censoring mechanisms. Simulation studies under a Clayton copula framework demonstrate that the proposed estimator gives unbiased estimates of PFS and cause-specific cumulative incidence functions with substantially reduced bias as compared to standard approaches. This work provides a practical and theoretically rigorous tool for analyzing interval-censored progression endpoints in oncology and offers a foundation for extensions to evaluation in the context of competing risks.

Nonparametric Maximum Likelihood Estimation


Progression-Free Survival


Competing Risks


Interval-Censored Data


Spline-Based Methods


Joint Modeling of Survival 

Biometrics Section