ArticleBiometrics2021
Causal mediation of semicompeting risks.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Non-Parametric Mediation Analysis of Non-Markov Illness-Death Model.Statistics in medicine · 2026Article
- Exploring Causal Effects of Hormone- and Radio-Treatments in an Observational Study of Breast Cancer Using Copula-Based Semi-Competing Risks Models.Statistics in medicine · 2025Article
- A Pseudo-Value Approach to Causal Deep Learning of Semi-Competing Risks.Arabian journal of mathematics · 2025Article
- Estimation of separable direct and indirect effects in a continuous-time illness-death model.Lifetime data analysis · 2024Article
- Surrogacy validation for time-to-event outcomes with illness-death frailty models.Biometrical journal. Biometrische Zeitschrift · 2024Article
- DEFINING AND ESTIMATING PRINCIPAL STRATUM SPECIFIC NATURAL MEDIATION EFFECTS WITH SEMI-COMPETING RISKS DATA.Statistica Sinica · 2023Article
- Exploring causal mechanisms and quantifying direct and indirect effects using a joint modeling approach for recurrent and terminal events.Statistics in medicine · 2023Article
- Translating questions to estimands in randomized clinical trials with intercurrent events.Statistics in medicine · 2022Article
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The semi-competing risks problem arises when one is interested in the effect of an exposure or treatment on both intermediate (e.g., having cancer) and primary events (e.g., death) where the intermediate event may be censored by the primary event, but not vice versa. Here we propose a nonparametric approach casting the semi-competing risks problem in the framework of causal mediation modeling. We set up a mediation model with the intermediate and primary events, respectively as the mediator and the outcome, and define an indirect effect as the effect of the exposure on the primary event mediated by the intermediate event and a direct effect as that not mediated by the intermediate event. A nonparametric estimator with time-varying weights is proposed for direct and indirect effects where the counting process at time t of the primary event
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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.