Assessment of an eye-tracking tool to discriminate between concussed and not concussed professional male rugby players: a cohort study.

Objectives: Concussion is a common injury in rugby union ('rugby') and yet its diagnosis is reliant on clinical judgment. Oculomotor testing could provide an objective measure to assist with concussion diagnosis. NeuroFlex® evaluates oculomotor function using a virtual-reality headset.

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.

Head Acceleration Events During Tackle, Ball-Carry, and Ruck Events in Professional Southern Hemisphere Men's Rugby Union Matches: A Study Using Instrumented Mouthguards.

Objectives: Describe head acceleration events (HAEs) experienced by professional male rugby union players during tackle, ball-carry, and ruck events using instrumented mouthguards (iMGs).

Design: Prospective observational cohort.

Methods: Players competing in the 2023 Currie Cup (141 players) and Super Rugby (66 players) seasons wore iMGs.

Testing the causal relationships of physical activity and sedentary behaviour with mental health and substance use disorders: a Mendelian randomisation study.

Observational studies suggest that physical activity can reduce the risk of mental health and substance use disorders. However, it is unclear whether this relationship is causal or explained by confounding bias (e.g.

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.

Hepatectomy risk assessment with functional magnetic resonance imaging (HEPARIM).

Background: Post hepatectomy liver failure (PHLF) remains a significant risk in patients undergoing curative liver resection for cancer, however currently available PHLF risk prediction investigations are not sufficiently accurate. The Hepatectomy risk assessment with functional magnetic resonance imaging trial (HEPARIM) aims to establish if quantitative MRI biomarkers of liver function & perfusion can be used to more accurately predict PHLF risk and FLR function, measured against indocyanine green (ICG) liver function test.

Methods: HEPARIM is an observational cohort study recruiting patients undergoing liver resection of 2 segments or more, prior to surgery patients will have both Dynamic Gadoxetate-enhanced (DGE) liver MRI and ICG testing.

Demonstration of functional rehabilitation treatment effects in children and young people after severe acquired brain injury.

Purpose: To examine relationships between functional outcomes after pediatric acquired brain injury (ABI) and measures of rehabilitation dose.

Methods: An observational study of children receiving residential neurorehabilitation after severe ABI.

Results: Basic total rehabilitation dose shows a paradoxical relationship to global outcome.

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.

Analyses of 'change scores' do not estimate causal effects in observational data.

Background: In longitudinal data, it is common to create 'change scores' by subtracting measurements taken at baseline from those taken at follow-up, and then to analyse the resulting 'change' as the outcome variable. In observational data, this approach can produce misleading causal-effect estimates. The present article uses directed acyclic graphs (DAGs) and simple simulations to provide an accessible explanation for why change scores do not estimate causal effects in observational data.

Latent class regression improves the predictive acuity and clinical utility of survival prognostication amongst chronic heart failure patients.

The present study aimed to compare the predictive acuity of latent class regression (LCR) modelling with: standard generalised linear modelling (GLM); and GLMs that include the membership of subgroups/classes (identified through prior latent class analysis; LCA) as alternative or additional candidate predictors. Using real world demographic and clinical data from 1,802 heart failure patients enrolled in the UK-HEART2 cohort, the study found that univariable GLMs using LCA-generated subgroup/class membership as the sole candidate predictor of survival were inferior to standard multivariable GLMs using the same four covariates as those used in the LCA. The inclusion of the LCA subgroup/class membership together with these four covariates as candidate predictors in a multivariable GLM showed no improvement in predictive acuity.

Early childhood weight gain: Latent patterns and body composition outcomes.

Background: Despite early childhood weight gain being a key indicator of obesity risk, we do not have a good understanding of the different patterns that exist.

Objectives: To identify and characterise distinct groups of children displaying similar early-life weight trajectories.

Methods: A growth mixture model captured heterogeneity in weight trajectories between 0 and 60 months in 1390 children in the Avon Longitudinal Study of Parents and Children.

Distinct Body Mass Index Trajectories to Young-Adulthood Obesity and Their Different Cardiometabolic Consequences.

[Figure: see text].

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.

Time to reality check the promises of machine learning-powered precision medicine.

Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would represent a revolution in clinical research and practice. However, although the vision of individually tailored medicine is alluring, there is a need to distinguish genuine potential from hype.

Reflection on modern methods: generalized linear models for prognosis and intervention-theory, practice and implications for machine learning.

Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Nevertheless, these two concepts are often conflated in practice.

Response to: Simpson's Paradox is suppression, but Lord's Paradox is neither: clarification of and correction to Tu, Gunnell, and Gilthorpe (2008) by Nickerson CA & Brown NJL (https://doi.org/10.1186/1742-7622-5-2).

We commend Nickerson and Brown on their insightful exposition of the mathematical algebra behind Simpson's paradox, suppression and Lord's paradox; we also acknowledge there can be differences in how Lord's paradox is approached analytically, compared to Simpson's paradox and suppression, though not in every example of Lord's paradox. Furthermore, Simpson's paradox, suppression and Lord's paradox ask the same questions, seeking to understand if statistical adjustment is valid and meaningful, identifying which analytical option is correct. In our exposition of this, we focus on the perspective of context, which must invoke causal thinking.

A causal inference perspective on the analysis of compositional data.

Background: Compositional 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).

PLATOON: Premature Loss of bAby Teeth and its impact On Orthodontic Need - protocol.

Objective: To investigate the impact of premature extraction of primary teeth (PEPT) on orthodontic treatment need in a cohort of children participating in the Born in Bradford (BiB) longitudinal birth cohort.

Design: Observational, cross-sectional cohort.

Participants: We aim to recruit 1000 children aged 7-11 years: 500 with a history of PEPT and 500 matched non-PEPT controls.

DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inference.

The current paradigm for causal inference in epidemiology relies primarily on the evaluation of counterfactual contrasts via statistical regression models informed by graphical causal models (often in the form of directed acyclic graphs, or DAGs) and their underlying mathematical theory. However, there have been growing calls for supplementary methods, and one such method that has been proposed is agent-based modelling due to its potential for simulating counterfactuals. However, within the epidemiological literature, there currently exists a general lack of clarity regarding what exactly agent-based modelling is (and is not) and, importantly, how it differs from microsimulation modelling-perhaps its closest methodological comparator.