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On Window Mean Survival Time With Interval-Censored Data.

Takuto Iijima1, Tomotaka Momozaki2, Shuji Ando2

  • 1Department of Information Sciences, Graduate School of Science and Technology, Tokyo University of Science, Chiba, Japan.

Statistics in Medicine
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Summary
This summary is machine-generated.

This study introduces a new method for estimating window mean survival time (WMST) in cancer clinical trials with interval-censored data. The proposed WMST method shows higher power than restricted mean survival time (RMST) in specific scenarios.

Keywords:
Kaplan–Meier methodmid‐point imputationnonparametric estimatornon‐proportional hazardssurvival data

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Survival Analysis

Background:

  • Cancer clinical trials increasingly face non-proportional hazards (NPH) scenarios, especially with immunotherapy.
  • Late difference and early crossing survival curves are common in immunotherapy trials, necessitating advanced statistical methods.
  • Existing methods like restricted mean survival time (RMST) have limitations in NPH scenarios.

Purpose of the Study:

  • To develop and evaluate a window mean survival time (WMST) estimation method for interval-censored data in cancer clinical trials.
  • To address the need for robust statistical tools in analyzing survival data with non-proportional hazards.
  • To compare the performance of the proposed WMST method against existing methods like RMST and the log-rank test.

Main Methods:

  • Proposed a WMST inference method using one-point imputations and Turnbull's method for interval-censored data.
  • Conducted extensive numerical simulations to assess the performance of the WMST estimation and testing methods.
  • Utilized mid-point imputation for standard error calculation, facilitating its adoption as a standard method.

Main Results:

  • The WMST estimation method using mid-point imputation demonstrated comparable performance to Turnbull's method for interval-censored data.
  • The proposed WMST testing method exhibited higher statistical power than RMST in late difference and early crossing survival curve scenarios.
  • WMST maintained higher power than RMST even when the pre-specified interval deviated from the clinically desirable time point, and showed compatible power to the log-rank test under proportional hazards.

Conclusions:

  • The developed WMST method provides a powerful and reliable tool for analyzing survival data in cancer clinical trials with NPH.
  • The proposed method offers advantages over RMST, particularly in scenarios with late differences or early crossing survival curves.
  • This research contributes to advancing statistical methodologies for cancer clinical trial analysis, especially with the rise of immunotherapy.