ArticleBiometrics2018
Analysis of restricted mean survival time for length-biased data.
Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of Postoperative Complications on Overall Survival Following Esophagectomy: A Meta-Analysis Using the Restricted Mean Survival Time Analysis.Thoracic cancer · 2025Pooled it
- Sex Differences in the Survival of Patients with Neuroendocrine Neoplasms: A Comparative Study of Two National Databases.Cancers · 2024Article
- Differences of survival benefits brought by various treatments in ovarian cancer patients with different tumor stages.Journal of ovarian research · 2023Article
- Regression modeling of restricted mean survival time for left-truncated right-censored data.Statistics in medicine · 2022Article
- Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma.Journal of translational medicine · 2022Article
- Nonparametric and semiparametric estimators of restricted mean survival time under length-biased sampling.Lifetime data analysis · 2020Article
- A technique for approximating transition rates from published survival analyses.Cost effectiveness and resource allocation : C/E · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In clinical studies with time-to-event outcomes, the restricted mean survival time (RMST) has attracted substantial attention as a summary measurement for its straightforward clinical interpretation. When the data are subject to length-biased sampling, which is frequently encountered in observational cohort studies, existing methods to estimate the RMST are not applicable. In this article, we consider nonparametric and semiparametric regression methods to estimate the RMST under the setting of length-biased sampling. To assess the covariate effects on the RMST, a semiparametric regression model that directly relates the covariates and the RMST is assumed. Based on the model, we develop unbiased estimating equations to obtain consistent estimators of covariate effects by properly adjusting for informative censoring and length bias. Stochastic process theories are used to establish the asymptotic properties of the proposed estimators. We investigate the finite sample performance through simulations and illustrate the methods by analyzing a prevalent cohort study of dementia in Canada.
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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.