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The difference in restricted mean survival time (RMST) is a clinically meaningful measure to quantify treatment effect in randomized controlled trials, especially when the proportional hazards assumption does not hold. Several frequentist…

In clinical or epidemiological follow-up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods,…

Methodology · Statistics 2022-05-26 Chengfeng Zhang , Hongji Wu , Baoyi Huang , Hao Yuan , Yawen Hou , Zheng Chen

The restricted mean survival time (RMST) is the mean survival time in the study population followed up to a specific time point, and is simply the area under the survival curve up to the specific time point. The difference between two RMSTs…

Methodology · Statistics 2025-09-18 Peter Zhang , Brent Logan , Michael Martens

The use of the non-parametric Restricted Mean Survival Time endpoint (RMST) has grown in popularity as trialists look to analyse time-to-event outcomes without the restrictions of the proportional hazards assumption. In this paper, we…

Methodology · Statistics 2023-11-06 Emily Alger , David S. Robertson , Abigail J. Burdon

Restricted mean survival time (RMST) is gaining attention as a measure to quantify the treatment effect on survival outcomes in randomized clinical trials. Several methods to determine sample size based on the RMST-based tests have been…

Methodology · Statistics 2022-12-19 Satoshi Hattori , Hajime Uno

The restricted mean survival time (RMST) model has been garnering attention as a way to provide a clinically intuitive measure: the mean survival time. RMST models, which use methods based on pseudo time-to-event values and inverse…

Methodology · Statistics 2024-06-11 Keisuke Hanada , Masahiro Kojima

The difference in restricted mean survival times between two groups is a clinically relevant summary measure. With observational data, there may be imbalances in confounding variables between the two groups. One approach to account for such…

Applications · Statistics 2014-10-17 Andrew Wey , David Vock , John Connett , Kyle Rudser

In clinical follow-up studies with a time-to-event end point, the difference in the restricted mean survival time (RMST) is a suitable substitute for the hazard ratio (HR). However, the RMST only measures the survival of patients over a…

Methodology · Statistics 2023-06-27 Zijing Yang , Chengfeng Zhang , Yawen Hou , Zheng Chen

The restricted mean survival time (RMST) is a widely used quantity in survival analysis due to its straightforward interpretation. For instance, predicting the time to event based on patient attributes is of great interest when analyzing…

Statistics Theory · Mathematics 2025-03-11 Ariane Cwiling , Vittorio Perduca , Olivier Bouaziz

Restricted mean survival time (RMST) offers a compelling nonparametric alternative to hazard ratios for right-censored time-to-event data, particularly when the proportional hazards assumption is violated. By capturing the total event-free…

Methodology · Statistics 2025-01-28 Jinghao Sun , Douglas E. Schaubel , Eric J. Tchetgen Tchetgen

The restricted mean survival time (RMST) has become a popular measure to summarize event times in longitudinal studies. Defined as the area under the survival function up to a time horizon $\tau$ > 0, the RMST can be interpreted as the life…

Methodology · Statistics 2024-11-05 Alina Schenk , Vanessa Basten , Matthias Schmid

In the process of clinical diagnosis and treatment, the restricted mean survival time (RMST), which reflects the life expectancy of patients up to a specified time, can be used as an appropriate outcome measure. However, the RMST only…

Applications · Statistics 2021-06-22 Zijing Yang , Hongji Wu , Yawen Hou , Hao Yuan , Zheng Chen

Restricted mean survival time (RMST) is an intuitive summary statistic for time-to-event random variables, and can be used for measuring treatment effects. Compared to hazard ratio, its estimation procedure is robust against the…

Methodology · Statistics 2023-05-25 Ruizhe Chen , Sanjib Basu , Qian Shi

Covariate adjustment aims to improve the statistical efficiency of randomized trials by incorporating information from baseline covariates. Popular methods for covariate adjustment include analysis of covariance for continuous endpoints and…

Methodology · Statistics 2025-05-09 Zhiwei Zhang , Ya Wang , Dong Xi

The widely used proportional hazard assumption cannot be assessed reliably in small-scale clinical trials and might often in fact be unjustified, e.g. due to delayed treatment effects. An alternative to the hazard ratio as effect measure is…

Methodology · Statistics 2024-08-29 David Jesse , Cynthia Huber , Tim Friede

The restricted mean survival time (RMST) difference offers an interpretable causal contrast to estimate the treatment effect for time-to-event outcomes, yet a wide range of available estimators leaves limited guidance for practice. We…

Methodology · Statistics 2026-03-02 Charlotte Voinot , Clément Berenfeld , Imke Mayer , Bernard Sebastien , Julie Josse

In large observational studies, the case-cohort design is commonly used to reduce the cost associated with covariate measurement. For survival outcomes, literature has suggested that the restricted mean survival time (RMST) be a more…

Methodology · Statistics 2026-05-08 Andy Ni , Wei-En Lu , Bo Lu

Restricted mean survival time (RMST) models have gained popularity when analyzing time-to-event outcomes because RMST models offer more straightforward interpretations of treatment effects with fewer assumptions than hazard ratios commonly…

Methodology · Statistics 2023-10-23 Kaiyuan Hua , Xiaofei Wang , Hwanhee Hong

Recent observations, especially in cancer immunotherapy clinical trials with time-to-event outcomes, show that the commonly used proportial hazard assumption is often not justifiable, hampering an appropriate analyse of the data by hazard…

Methodology · Statistics 2021-02-23 Marc Ditzhaus , Menggang Yu , Jin Xu

Comparing survival experiences of different groups of data is an important issue in several applied problems. A typical example is where one wishes to investigate treatment effects. Here we propose a new Bayesian approach based on…

Methodology · Statistics 2024-07-17 Alan Riva-Palacio , Fabrizio Leisen , Antonio Lijoi
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