Depicting deterministic variables within directed acyclic graphs (DAGs): An aid for identifying and interpreting causal effects involving derived variables and compositional data.

Deterministic variables are variables that are functionally determined by one or more parent variables. They commonly arise when a variable has been functionally created from one or more parent variables, as with derived variables, and in compositional data, where the 'whole' variable is determined from its 'parts'. This article introduces how deterministic variables may be depicted within directed acyclic graphs (DAGs) to help with identifying and interpreting causal effects involving derived variables and/or compositional data.

Theory and performance of substitution models for estimating relative causal effects in nutritional epidemiology.

Background: Estimating relative causal effects (i.e., "substitution effects") is a common aim of nutritional research.

Estimating the effects of lockdown timing on COVID-19 cases and deaths in England: A counterfactual modelling study.

Background: During the first wave of the COVID-19 pandemic, the United Kingdom experienced one of the highest per-capita death tolls worldwide. It is debated whether this may partly be explained by the relatively late initiation of voluntary social distancing and mandatory lockdown measures. In this study, we used simulations to estimate the number of cases and deaths that would have occurred in England by 1 June 2020 if these interventions had been implemented one or two weeks earlier, and the impact on the required duration of lockdown.

Adjustment for energy intake in nutritional research: a causal inference perspective.

Background: Four models are commonly used to adjust for energy intake when estimating the causal effect of a dietary component on an outcome: 1) the "standard model" adjusts for total energy intake, 2) the "energy partition model" adjusts for remaining energy intake, 3) the "nutrient density model" rescales the exposure as a proportion of total energy, and 4) the "residual model" indirectly adjusts for total energy by using a residual. It remains underappreciated that each approach evaluates a different estimand and only partially accounts for confounding by common dietary causes.

Objectives: We aimed to clarify the implied causal estimand and interpretation of each model and evaluate their performance in reducing dietary confounding.

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations.

Background: Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research.

Methods: Original health research articles published during 1999-2017 mentioning 'directed acyclic graphs' (or similar) or citing DAGitty were identified from Scopus, Web of Science, Medline and Embase.

Relation between hemoglobin a(1c) and outcomes in heart failure patients with and without diabetes mellitus.

In patients with diabetes mellitus (DM) in the general population, elevated glycosylated hemoglobin (HbA(1c)) increases the risk for developing heart failure (HF). However, in patients with established HF, the association of HbA(1c) level with outcomes is not well established. The aim of this study was to investigate the relation between HbA(1c) and outcomes in patients with HF with and without DM.