ArticleBMC medical research methodology2020
The use of restricted mean time lost under competing risks data.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Multi-group global tests for restricted mean survival time and restricted mean time lost: a variable transformation approach.BMC medical research methodology · 2025Article
- Emulating the EPIC trial using VetCompass primary-care data: causal effects of pimobendan in UK dogs with grade IV/VI heart murmurs.PloS one · 2025Article
- Assessing treatment effects with adjusted restricted mean time lost in observational competing risks data.BMC medical research methodology · 2024Article
- Role of arrhythmic phenotype in prognostic stratification and management of dilated cardiomyopathy.European journal of heart failure · 2024Article
- Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale.Frontiers in oncology · 2024Article
- Studentized permutation method for comparing two restricted mean survival times with small sample from randomized trials.Statistics in medicine · 2023Article
- Implementation of an Alternative Method for Assessing Competing Risks: Restricted Mean Time Lost.American journal of epidemiology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundUnder competing risks, the commonly used sub-distribution hazard ratio (SHR) is not easy to interpret clinically and is valid only under the proportional sub-distribution hazard (SDH) assumption. This paper introduces an alternative statistical measure: the restricted mean time lost (RMTL).
methodsFirst, the definition and estimation methods of the measures are introduced. Second, based on the differences in RMTLs, a basic difference test (Diff) and a supremum difference test (sDiff) are constructed. Then, the corresponding sample size estimation method is proposed. The statistical properties of the methods and the estimated sample size are evaluated using Monte Carlo simulations, and these methods are also applied to two real examples.
resultsThe simulation results show that sDiff performs well and has relatively high test efficiency in most situations. Regarding sample size calculation, sDiff exhibits good performance in various situations. The methods are illustrated using two examples.
conclusionsRMTL can meaningfully summarize treatment effects for clinical decision making, which can then be reported with the SDH ratio for competing risks data. The proposed sDiff test and the two calculated sample size formulas have wide applicability and can be considered in real data analysis and trial design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.