ArticleInternational journal of epidemiology2020
A causal inference perspective on the analysis of compositional data.
Article in International journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Dietary intake and sources of sodium and potassium, and salt-related knowledge, attitudes, and behaviours among rural adults in China: a cross-sectional study.Frontiers in nutrition · 2026Article
- Article
- Beyond "validated": the need for more rigorous reporting of food frequency questionnaire validation metrics in nutritional epidemiology.European journal of nutrition · 2025Article
- Impact of daily activity allocation on problematic internet use among university students: A compositional perspective.Journal of behavioral addictions · 2025Article
- A comparison of methods for analysing compositional data with fixed and variable totals: a simulation study using the examples of time-use and dietary data.BMC medical research methodology · 2025Article
- Instrumental variable estimation for compositional treatments.Scientific reports · 2025Article
- Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional data.American journal of epidemiology · 2025Article
- Relationship Among Macronutrients, Dietary Components, and Objective Sleep Variables Measured by Smartphone Apps: Real-World Cross-Sectional Study.Journal of medical Internet research · 2025Article
- Racial and ethnic differences in the risk of dementia diagnosis under hypothetical blood pressure-lowering interventions: The Multi-Ethnic Study of Atherosclerosis.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Longitudinal associations of screen time, physical activity, and sleep duration with body mass index in U.S. youth.The international journal of behavioral nutrition and physical activity · 2024Article
- Substitution analyses of foods with varying fat quality and the associations with all-cause mortality and impact of the FADS-1 genotype in elderly men.European journal of nutrition · 2024Article
- Association between protein intake, diet quality, and obesity in Australian adults: a comparison of measurement units.Journal of nutritional science · 2024Article
- The association between dietary amino acid profile and the risk of type 2 diabetes: Ravansar non-communicable disease cohort study.BMC public health · 2023Article
- Socio-economic and ethnic disparities in childhood cancer survival, Yorkshire, UK.British journal of cancer · 2023Article
- Exploring the association of dietary patterns with the risk of hypertension using principal balances analysis and principal component analysis.Public health nutrition · 2023Article
- The immune factors driving DNA methylation variation in human blood.Nature communications · 2022Article
- Association of sweetened carbonated beverage consumption during pregnancy and ADHD symptoms in the offspring: a study from the Norwegian Mother, Father and Child Cohort Study (MoBa).European journal of nutrition · 2022Article
- Adjustment for energy intake in nutritional research: a causal inference perspective.The American journal of clinical nutrition · 2022Article
- Joint association between accelerometry-measured daily combination of time spent in physical activity, sedentary behaviour and sleep and all-cause mortality: a pooled analysis of six prospective cohorts using compositional analysis.British journal of sports medicine · 2021Article
- Cross-sectional and prospective associations of sleep, sedentary and active behaviors with mental health in older people: a compositional data analysis from the Seniors-ENRICA-2 study.The international journal of behavioral nutrition and physical activity · 2021Article
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Funding
Abstract
backgroundCompositional data comprise the parts of some whole, for which all parts sum to that whole. They are prevalent in many epidemiological contexts. Although many of the challenges associated with analysing compositional data have been discussed previously, we do so within a formal causal framework by utilizing directed acyclic graphs (DAGs).
methodsWe depict compositional data using DAGs and identify two distinct effect estimands in the generic case: (i) the total effect, and (ii) the relative effect. We consider each in the context of three specific example scenarios involving compositional data: (1) the relationship between the economically active population and area-level gross domestic product; (2) the relationship between fat consumption and body weight; and (3) the relationship between time spent sedentary and body weight. For each, we consider the distinct interpretation of each effect, and the resulting implications for related analyses.
resultsFor scenarios (1) and (2), both the total and relative effects may be identifiable and causally meaningful, depending upon the specific question of interest. For scenario (3), only the relative effect is identifiable. In all scenarios, the relative effect represents a joint effect, and thus requires careful interpretation.
conclusionsDAGs are useful for considering causal effects for compositional data. In all analyses involving compositional data, researchers should explicitly consider and declare which causal effect is sought and how it should be interpreted.
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