ArticleBiometrics2021
Discussion on "Causal mediation of semicompeting risks" by Yen-Tsung Huang.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Non-Parametric Mediation Analysis of Non-Markov Illness-Death Model.Statistics in medicine · 2026Article
- Separability and identifiability as primary obstacles to substantively useful mediation analysis.European journal of epidemiology · 2026Article
- Estimation of separable direct and indirect effects in a continuous-time illness-death model.Lifetime data analysis · 2024Article
- Methods of analysis for survival outcomes with time-updated mediators, with application to longitudinal disease registry data.Statistical methods in medical research · 2022Article
- Translating questions to estimands in randomized clinical trials with intercurrent events.Statistics in medicine · 2022Article
Corrections and comments
- Commented on by
- Comment on
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Huang proposes a method for assessing the impact of a point treatment on mortality either directly or mediated by occurrence of a nonterminal health event, based on data from a prospective cohort study in which the occurrence of the nonterminal health event may be preemptied by death but not vice versa. The author uses a causal mediation framework to formally define causal quantities known as natural (in)direct effects. The novelty consists of adapting these concepts to a continuous-time modeling framework based on counting processes. In an effort to posit "scientifically interpretable estimands," statistical and causal assumptions are introduced for identification. In this commentary, we argue that these assumptions are not only difficult to interpret and justify, but are also likely violated in the hepatitis B motivating example and other survival/time to event settings as well.
Indexed as
Identifiers
34510405What OpenQuestion holds
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.