Nonparametric estimation of the Patient Weighted While-Alive Estimand

2024-12-04

AI Summary

In clinical trials with recurrent events (e.g., repeated hospitalizations) subject to competing death, conventional event-rate estimands suffer from underestimation due to early attrition, impeding valid treatment effect assessment. To address this, we propose the “patient-weighted alive-time event rate”—a while-alive estimand defined as the expected number of events within a target window divided by total observed person-time-at-risk. We derive its efficient influence function for the first time and construct a novel nonparametric estimator tailored to randomized trials, circumventing computational infeasibility of one-step methods in recurrent-event settings. The estimator is proven to achieve the semiparametric efficiency bound; its asymptotic optimality is established under an irreversible illness–death model. Applied to real-world metastatic colorectal cancer data, it effectively corrects early-death bias, enhancing both reliability and interpretability of treatment effect estimation.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In clinical trials with recurrent events, such as repeated hospitalizations terminating with death, it is important to consider the patient events overall history for a thorough assessment of treatment effects. The occurrence of fewer events due to early deaths can lead to misinterpretation, emphasizing the importance of a while-alive strategy as suggested in Schmidli et al. (2023). We focus in this paper on the patient weighted while-alive estimand represented as the expected number of events divided by the time alive within a target window and develop efficient estimation for this estimand. We derive its efficient influence function and develop a one-step estimator, initially applied to the irreversible illness-death model. For the broader context of recurrent events, due to the increased complexity, the one-step estimator is practically intractable. We therefore suggest an alternative estimator that is also expected to have high efficiency focusing on the randomized treatment setting. We compare the efficiency of these two estimators in the illness-death setting. Additionally, we apply our proposed estimator to a real-world case study involving metastatic colorectal cancer patients, demonstrating the practical applicability and benefits of the while-alive approach.

Problem

• Estimating patient weighted while-alive estimand in clinical trials
• Addressing misinterpretation from fewer events due to early deaths
• Developing efficient estimators for recurrent events with death

Innovation

• Nonparametric estimation of patient weighted while-alive estimand
• Developed efficient one-step estimator for irreversible illness-death model
• Proposed alternative high-efficiency estimator for randomized treatment setting