ArticleLifetime data analysis2021
A generalized theory of separable effects in competing event settings.
Article in Lifetime data analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
18 citing papers in PubMed.
- Causal effect on the number of life years lost due to a specific event: average treatment effect and variable importance.Lifetime data analysis · 2026Article
- A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.Statistics in medicine · 2026Article
- Illustrating Implications of Misaligned Causal Questions and Statistics in Settings With Competing Events and Interest in Treatment Mechanisms.Statistics in medicine · 2026Article
- The role of coronary artery calcification in metal-related cardiovascular disease.Environment international · 2026Article
- Recanting Twins: Addressing Intermediate Confounding in Mediation Analysis.Statistics in medicine · 2026Article
- Why use methods that require proportional hazards?American journal of epidemiology · 2025Article
- 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
- Subjective Cognitive Impairment (SCI) and Future Dementia Risk in the National Health and Aging Trends Study (NHATS) During 2012-2019.Journal of aging and health · 2025Article
- Inverse Probability of Treatment Weighting Using the Propensity Score With Competing Risks in Survival Analysis.Statistics in medicine · 2025Article
- Longitudinal mediation analysis with multilevel and latent growth models: a separable effects causal approach.BMC medical research methodology · 2024Article
- TESTING FOR THE CAUSAL MEDIATION EFFECTS OF MULTIPLE MEDIATORS USING THE KERNEL MACHINE DIFFERENCE METHOD IN GENOME-WIDE EPIGENETIC STUDIES.The annals of applied statistics · 2024Article
- Selection Bias in Health Research: Quantifying, Eliminating, or Exacerbating Health Disparities?Current epidemiology reports · 2024Article
- Estimation of separable direct and indirect effects in a continuous-time illness-death model.Lifetime data analysis · 2024Article
- Causal inference with recurrent and competing events.Lifetime data analysis · 2024Article
- Considering Questions Before Methods in Dementia Research With Competing Events and Causal Goals.American journal of epidemiology · 2023Article
- Initiation of Antihypertensive Medication from Midlife on Incident Dementia: The Health and Retirement Study.Journal of Alzheimer's disease : JAD · 2023Observational
- Estimating causal effects in the presence of competing events using regression standardisation with the Stata command standsurv.BMC medical research methodology · 2022Article
- Translating questions to estimands in randomized clinical trials with intercurrent events.Statistics in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However, effects of treatment on the competing event may indirectly contribute to this total effect, complicating its interpretation. We previously proposed the separable effects to define direct and indirect effects of the treatment on the event of interest. This definition was given in a simple setting, where the treatment was decomposed into two components acting along two separate causal pathways. Here we generalize the notion of separable effects, allowing for interpretation, identification and estimation in a wide variety of settings. We propose and discuss a definition of separable effects that is applicable to general time-varying structures, where the separable effects can still be meaningfully interpreted as effects of modified treatments, even when they cannot be regarded as direct and indirect effects. For these settings we derive weaker conditions for identification of separable effects in studies where decomposed, or otherwise modified, treatments are not yet available; in particular, these conditions allow for time-varying common causes of the event of interest, the competing events and loss to follow-up. We also propose semi-parametric weighted estimators that are straightforward to implement. We stress that unlike previous definitions of direct and indirect effects, the separable effects can be subject to empirical scrutiny in future studies.
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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.