Cure Rate Joint Model for Time-to-Event Data and Longitudinal Tumor Burden with Potential Change Points

2025-07-24

AI Summary

To address the challenge of jointly modeling tumor burden (TB) dynamics and time-to-progression in non-small cell lung cancer (NSCLC) clinical trials, we propose a novel joint model that identifies patient-specific change points to accurately characterize the biphasic TB trajectory—initial decline followed by rebound—and classifies patients as “progressing” or “stable.” Our method innovatively incorporates a cure-rate framework and individualized change-point estimation, integrating longitudinal TB measurements with event-time data to flexibly capture heterogeneous disease evolution while robustly handling censoring. Parameters are estimated via a Monte Carlo expectation-maximization (MCEM) algorithm. Applied to a Phase III trial, the model demonstrates that cemiplimab monotherapy significantly prolongs the duration and deepens the magnitude of TB decline, yielding more reliable marginal efficacy estimates. This work establishes a new methodological framework for dynamic evaluation of immunotherapy treatment effects.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

In non-small cell lung cancer (NSCLC) clinical trials, tumor burden (TB) is a key longitudinal biomarker for assessing treatment effects. Typically, standard-of-care (SOC) therapies and some novel interventions initially decrease TB; however, many patients subsequently experience an increase-indicating disease progression-while others show a continuous decline. In patients with an eventual TB increase, the change point marks the onset of progression and must occur before the time of the event. To capture these distinct dynamics, we propose a novel joint model that integrates time-to-event and longitudinal TB data, classifying patients into a change-point group or a stable group. For the change-point group, our approach flexibly estimates an individualized change point by leveraging time-to-event information. We use a Monte Carlo Expectation-Maximization (MCEM) algorithm for efficient parameter estimation. Simulation studies demonstrate that our model outperforms traditional approaches by accurately capturing diverse disease progression patterns and handling censoring complexities, leading to robust marginal TB outcome estimates. When applied to a Phase 3 NSCLC trial comparing cemiplimab monotherapy to SOC, the treatment group shows prolonged TB reduction and consistently lower TB over time, highlighting the clinical utility of our approach. The implementation code is publicly available on https://github.com/quyixiang/JoCuR.

Problem

• Modeling tumor burden dynamics with change points in NSCLC
• Integrating time-to-event and longitudinal data for progression analysis
• Improving accuracy in estimating disease progression patterns

Innovation

• Joint model for time-to-event and longitudinal data
• Monte Carlo EM algorithm for parameter estimation
• Individualized change point estimation for progression