ArticlePloS one2015
Testing for mechanistic interactions in long-term follow-up studies.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Association between IL23R and ERAP1 polymorphisms and sacroiliac or spinal MRI inflammation in spondyloarthritis: DESIR cohort data.Arthritis research & therapy · 2019Trial
- Do Single Food Habits Matter? Fish and Vegetables Intake and Risk of Low HRQoL in Schoolchildren (ASOMAD Study).Children (Basel, Switzerland) · 2025Article
- A self-consistent probabilistic formulation for inference of interactions.Scientific reports · 2020Article
- Sharp bounds on sufficient-cause interactions under the assumption of no redundancy.BMC medical research methodology · 2017Article
- Complementary Log Regression for Sufficient-Cause Modeling of Epidemiologic Data.Scientific reports · 2016Article
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In follow-up studies, interactions are often assessed by including a cross-product term in a (multiplicative) Cox model. However, epidemiologists/clinicians often misinterpret a significant multiplicative interaction as a genuine mechanistic interaction. Though indices specific to mechanistic interactions have been proposed, including the 'relative excess risk due to interaction' (RERI) and the 'peril ratio index of synergy based on multiplicativity' (PRISM), these indices assume no loss to follow up and no competing death in a study. In this paper, the authors propose a novel 'mechanistic interaction test' (MIT) for censored data. Monte-Carlo simulation shows that when the hazard curves are proportional to, non-proportional to, or even crossing over one another, the proposed MIT can maintain reasonably accurate type I error rates for censored data. It has far greater powers than the modified RERI and PRISM tests (modified for censored data scenarios). To test mechanistic interactions in censored data, we recommend using MIT in light of its desirable statistical properties.
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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.