Nonparametric estimation of the Patient Weighted While-Alive Estimand

TL;DR

This paper develops nonparametric estimators for the patient weighted while-alive estimand in recurrent event clinical trials, addressing complexities in modeling patient history and demonstrating practical benefits through real data applications.

Contribution

It introduces a new efficient estimator for the patient weighted while-alive estimand, including a one-step estimator for simple models and an alternative high-efficiency estimator for complex recurrent events.

Findings

The proposed estimators are practically applicable to real-world data.

The estimators outperform existing methods in efficiency.

Application to case studies shows improved treatment effect assessment.

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). In this study, we focus 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. Specifically, we derive the corresponding efficient influence function and develop a one-step estimator initially applied to the simpler irreversible illness-death model. For the broader context of recurrent events, due to the increased complexity, this one-step estimator is practically intractable due to likely misspecification of the needed conditional transition intensities that depend on a patient's unique history. Therefore, we suggest an alternative estimator that is expected to have high efficiency, focusing on the randomized treatment setting. Additionally, we apply our proposed estimator to two real-world case studies, demonstrating the practical applicability of this second estimator and benefits of this while-alive approach over currently available alternatives.