ArticleStatistical methods in medical research2022
Integrating relative survival in multi-state models-a non-parametric approach.
Article in Statistical methods in medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Age at Menopause and Trajectories of Multimorbidity Progression to Mortality: A Multi-State Analysis of UK Biobank Data.BJOG : an international journal of obstetrics and gynaecology · 2026Article
- Censoring, Competing Events, and Multistate Models: Comment on Beyersmann et al. "Hazards Constitute Key Quantities for Analyzing, Interpreting and Understanding Time-to-Event Data".Biometrical journal. Biometrische Zeitschrift · 2026Article
- Dynamics of infection, vaccination and excess mortality during the COVID-19 pandemic among older individuals-a nationwide analysis.European journal of epidemiology · 2026Article
- Associations between the C-reactive protein-triglyceride glucose index and the incidence and progression trajectory of cardiometabolic multimorbidity: a multi-state model study.Cardiovascular diabetology · 2026Article
- The impact of cancer survivors' extra risk of noncancer mortality on net survival estimation.American journal of epidemiology · 2025Article
- Analysis of Log Data From an International Online Educational Assessment System: A Multi-State Survival Modeling Approach to Reaction Time Between and Across Action Sequence.Psychometrika · 2025Article
- A Multistate Model Incorporating Relative Survival Extrapolation and Mixed Time Scales for Health Technology Assessment.PharmacoEconomics · 2025Article
- Article
- Allogeneic hematopoietic stem cell transplantation in patients aged 60-79 years in Germany (1998-2018): a registry study.Haematologica · 2024Article
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Authors and funding
6 authors.
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Abstract
Multi-state models provide an extension of the usual survival/event-history analysis setting. In the medical domain, multi-state models give the possibility of further investigating intermediate events such as relapse and remission. In this work, a further extension is proposed using relative survival, where mortality due to population causes (i.e. non-disease-related mortality) is evaluated. The objective is to split all mortality in disease and non-disease-related mortality, with and without intermediate events, in datasets where cause of death is not recorded or is uncertain. To this end, population mortality tables are integrated into the estimation process, while using the basic relative survival idea that the overall mortality hazard can be written as a sum of a population and an excess part. Hence, we propose an upgraded non-parametric approach to estimation, where population mortality is taken into account. Precise definitions and suitable estimators are given for both the transition hazards and probabilities. Variance estimating techniques and confidence intervals are introduced and the behaviour of the new method is investigated through simulations. The newly developed methodology is illustrated by the analysis of a cohort of patients followed after an allogeneic hematopoietic stem cell transplantation. The work has been implemented in the R package mstate.
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