ArticleLifetime data analysis2019
Defining causal mediation with a longitudinal mediator and a survival outcome.
Article in Lifetime data analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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.
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Who cites it
25 citing papers in PubMed.
- Mediation effects that emulate a target randomised trial: Simulation-based evaluation of ill-defined interventions on multiple mediators.Statistical methods in medical research · 2021Trial
- Illustrating Implications of Misaligned Causal Questions and Statistics in Settings With Competing Events and Interest in Treatment Mechanisms.Statistics in medicine · 2026Article
- Mediation analysis in longitudinal intervention studies with an ordinal treatment-dependent confounder.Statistical methods in medical research · 2026Article
- Inference About Separable Causal Effects With Longitudinal Bivariate Ordinal Responses With Missingness and Censoring.Statistics in medicine · 2026Article
- Robust evaluation of longitudinal surrogate markers with censored data.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Hazards Constitute Key Quantities for Analyzing, Interpreting and Understanding Time-to-Event Data.Biometrical journal. Biometrische Zeitschrift · 2025Review
- Longitudinal mediation analysis with multilevel and latent growth models: a separable effects causal approach.BMC medical research methodology · 2024Article
- Mediation analysis in longitudinal study with high-dimensional methylation mediators.Briefings in bioinformatics · 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
- 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
- A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior.Journal of agricultural, biological, and environmental statistics · 2023Article
- Methods of analysis for survival outcomes with time-updated mediators, with application to longitudinal disease registry data.Statistical methods in medical research · 2022Article
- Quantifying direct and indirect effect for longitudinal mediator and survival outcome using joint modeling approach.Biometrics · 2022Article
- Translating questions to estimands in randomized clinical trials with intercurrent events.Statistics in medicine · 2022Article
- Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality.Psychometrika · 2022Article
- Article
- Mediation analysis for survival data with high-dimensional mediators.Bioinformatics (Oxford, England) · 2021Article
- A generalized theory of separable effects in competing event settings.Lifetime data analysis · 2021Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the context of causal mediation analysis, prevailing notions of direct and indirect effects are based on nested counterfactuals. These can be problematic regarding interpretation and identifiability especially when the mediator is a time-dependent process and the outcome is survival or, more generally, a time-to-event outcome. We propose and discuss an alternative definition of mediated effects that does not suffer from these problems, and is more transparent than the current alternatives. Our proposal is based on the extended graphical approach of Robins and Richardson (in: Shrout (ed) Causality and psychopathology: finding the determinants of disorders and their cures, Oxford University Press, Oxford, 2011), where treatment is decomposed into different components, or aspects, along different causal paths corresponding to real world mechanisms. This is an interesting alternative motivation for any causal mediation setting, but especially for survival outcomes. We give assumptions allowing identifiability of such alternative mediated effects leading to the familiar mediation g-formula (Robins in Math Model 7:1393, 1986); this implies that a number of available methods of estimation can be applied.
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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.