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RMST for Interval-Censored Data in Oncology Clinical Trials
Xiyuan Gao1, Tianmeng Lyu2, Menghao Xu3
1Department of Statistics, University of Missouri-Columbia, Columbia, Missouri, USA.
Statistics in Medicine
|February 8, 2025
Summary
Restricted mean survival time (RMST) offers a robust alternative to hazard ratios in oncology trials. New methods accurately estimate RMST with interval-censored progression-free survival (PFS) data, improving accuracy over conventional approaches.
Area of Science:
- Biostatistics
- Clinical Oncology
- Survival Analysis
Background:
- Proportional hazards assumption is often violated in oncology studies due to cured patients, delayed treatment effects, or switching.
- Restricted Mean Survival Time (RMST) is a clinically meaningful summary measure of treatment benefit, avoiding proportional hazards assumptions.
- Progression-Free Survival (PFS) is a common endpoint but involves interval-censored data for disease progression, complicating analysis.
Purpose of the Study:
- To develop and evaluate novel methods for estimating RMST with mixed right-censored and interval-censored data, common in oncology endpoints like PFS.
- To compare the performance of these new RMST estimators against conventional methods using simulations.
- To investigate the impact of assessment frequency, timing, and the choice of restricted time point on RMST estimation.
Main Methods:
- Simulation studies were conducted using various oncology trial scenarios, including different assessment frequencies and treatment effect sizes.
- Proposed alternative estimation and inference approaches for RMST accounting for interval-censored data.
- Evaluated RMST estimators under different patient visit schedules and time restrictions.
Main Results:
- RMST estimators incorporating interval censoring were found to be unbiased and more accurate than conventional methods.
- The performance of the proposed estimators in two-group comparisons was comparable to existing methods.
- The accuracy of the RMST estimators was influenced by the scheduled assessment plan and patient visit windows.
Conclusions:
- New RMST estimation methods provide unbiased and more accurate results for oncology trials with interval-censored PFS data.
- These advanced methods offer a reliable alternative to traditional approaches, especially when proportional hazards assumptions are questionable.
- The choice of assessment schedule and patient visit patterns significantly impacts the performance of RMST estimators.
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