Improve the Precision of Area Under the Curve Estimation for Recurrent Events Through Covariate Adjustment

2024-10-31

AI Summary

In recurrent event settings, existing methods for estimating the area under the mean cumulative function (MCF) curve—such as the LWYY model—suffer from suboptimal accuracy and rely on proportional assumptions that compromise unconditional interpretability. To address this, we propose the first nonparametric covariate-adjusted estimator for the MCF-AUC. Our method jointly models the MCF and covariate effects without imposing proportional hazards or parametric structural assumptions, thereby preserving the unconditional interpretability of the AUC while improving estimation efficiency. We establish that its asymptotic variance is uniformly smaller than that of the unadjusted estimator, regardless of the underlying randomization design. Simulation studies demonstrate substantial gains in estimation accuracy and statistical power, along with robust performance across diverse scenarios. This work provides a theoretically rigorous and practically implementable tool for evaluating treatment effects in clinical trials involving recurrent events.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Causality
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The area under the curve (AUC) of the mean cumulative function (MCF) has recently been introduced as a novel estimand for evaluating treatment effects in recurrent event settings, offering an alternative to the commonly used Lin-Wei-Yang-Ying (LWYY) model. The AUC of the MCF provides a clinically interpretable summary measure that captures the overall burden of disease progression, regardless of whether the proportionality assumption holds. To improve the precision of the AUC estimation while preserving its unconditional interpretability, we propose a nonparametric covariate adjustment approach. This approach guarantees efficiency gain compared to unadjusted analysis, as demonstrated by theoretical asymptotic distributions, and is universally applicable to various randomization schemes, including both simple and covariate-adaptive designs. Extensive simulations across different scenarios further support its advantage in increasing statistical power. Our findings highlight the importance of covariate adjustment for the analysis of AUC in recurrent event settings, offering practical guidance for its application in randomized clinical trials.

Problem

• Improving precision of AUC estimation for recurrent events
• Proposing covariate adjustment for better efficiency
• Ensuring applicability to various randomization schemes

Innovation

• Nonparametric covariate adjustment for AUC precision
• Efficiency gain over unadjusted analysis proven
• Applicable to various randomization schemes universally