ArticleBMC medical research methodology2017
Incorporating nonlinearity into mediation analyses.
Article in BMC medical research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Evaluating the Let's Move It intervention programme theory for adolescents' physical activity: Theorized psychosocial mechanisms of behavioural changes.British journal of health psychology · 2025Trial
- Inverted U-Shaped Association of Plasma Resolvin D2 With Atherosclerotic Cardiovascular Disease and the Mediation Effects of Serum Cholesterol: A Chinese Community-Based Study.Journal of the American Heart Association · 2024Article
- Studying Behaviour Change Mechanisms under Complexity.Behavioral sciences (Basel, Switzerland) · 2021Article
- The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time.Environment international · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundMediation is an important issue considered in the behavioral, medical, and social sciences. It addresses situations where the effect of a predictor variable X on an outcome variable Y is explained to some extent by an intervening, mediator variable M. Methods for addressing mediation have been available for some time. While these methods continue to undergo refinement, the relationships underlying mediation are commonly treated as linear in the outcome Y, the predictor X, and the mediator M. These relationships, however, can be nonlinear. Methods are needed for assessing when mediation relationships can be treated as linear and for estimating them when they are nonlinear.
methodsExisting adaptive regression methods based on fractional polynomials are extended here to address nonlinearity in mediation relationships, but assuming those relationships are monotonic as would be consistent with theories about directionality of such relationships.
resultsExample monotonic mediation analyses are provided assessing linear and monotonic mediation of the effect of family functioning (X) on a child's adaptation (Y) to a chronic condition by the difficulty (M) for the family in managing the child's condition. Example moderated monotonic mediation and simulation analyses are also presented.
conclusionsAdaptive methods provide an effective way to incorporate possibly nonlinear monotonicity into mediation relationships.
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Registered trials
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