On window mean survival time with interval-censored data

TL;DR

This paper develops a new method for estimating window mean survival time (WMST) with interval-censored data, demonstrating improved power over RMST in non-proportional hazards scenarios common in cancer immunotherapy trials.

Contribution

It introduces a WMST inference approach using one-point imputation and Turnbull's method, with extensive simulations showing superior power in specific survival scenarios.

Findings

WMST estimation with mid-point imputation performs comparably to Turnbull's method.

Proposed WMST testing has higher power than RMST in late difference and early crossing scenarios.

WMST maintains higher power than RMST even when $ au_0$ deviates from the optimal time point.

Abstract

In recent years, cancer clinical trials have increasingly encountered non proportional hazards (NPH) scenarios, particularly with the emergence of immunotherapy. In randomized controlled trials comparing immunotherapy with conventional chemotherapy or placebo, late difference and early crossing survivals scenarios are commonly observed. In such cases, window mean survival time (WMST), the area under the survival curve within a pre-specified interval $[\tau_0, \tau_1]$, has gained increasing attention due to its superior power compared to restricted mean survival time (RMST), the area under the survival curve up to a pre-specified time point. Considering the increasing use of progression-free survival as a co-primary endpoint alongside overall survival, there is a critical need to establish a WMST estimation method for interval-censored data; however, sufficient research has yet to be conducted. To bridge this gap, this study proposes a WMST inference method utilizing one-point imputations and Turnbull's method. Extensive numerical simulations demonstrate that the WMST estimation method using mid-point imputation for interval-censored data exhibits comparable performance to that using Turnbull's method. Since the former facilitates standard error calculation, we adopt it as the standard method. Numerical simulations on two-sample tests confirm that the proposed WMST testing method have higher power than RMST in late difference and early crossing survival scenarios, while having compatible power to the log-rank test under the PH. Furthermore, even when pre-specified $\tau_0$ deviated from the clinically desirable time point, WMST consistently maintains higher power than RMST in late difference and early crossing survivals scenarios.