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.

Causal and Associational Language in Observational Health Research: A Systematic Evaluation.

We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations.

Avoiding overadjustment bias in social epidemiology through appropriate covariate selection: a primer.

Obtaining accurate estimates of the causal effects of socioeconomic position (SEP) on health is important for public health interventions. To do this, researchers must identify and adjust for all potential confounding variables, while avoiding inappropriate adjustment for mediator variables on a causal pathway between the exposure and outcome. Unfortunately, 'overadjustment bias' remains a common and under-recognized problem in social epidemiology.

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.

Choice of Outcome in COVID-19 Studies and Implications for Policy: Mortality and Fatality.

In this brief communication, we discuss the confusion of mortality with fatality in the interpretation of evidence in the coronavirus disease 2019 (COVID-19) pandemic, and how this confusion affects the translation of science into policy and practice. We discuss how this confusion has influenced COVID-19 policy in France, Sweden, and the United Kingdom and discuss the implications for decision-making about COVID-19 vaccine distribution. We also discuss how this confusion is an example of a more general statistical fallacy we term the "Missing Link Fallacy.

Fasting plasma glucose, diagnosis of gestational diabetes and the risk of large for gestational age: a regression discontinuity analysis of routine data.

Objective: To estimate the causal effects of fasting plasma glucose (FPG) and diagnosis of gestational diabetes (GDM) on birthweight and the risks of large for gestational age (LGA).

Design: Regression discontinuity analysis of routine data.

Setting: Two district general hospitals in West Yorkshire, UK.

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.

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.

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).

Gestational diabetes and the risk of late stillbirth: a case-control study from England, UK.

Objective: To explore the separate effects of being 'at risk' of gestational diabetes mellitus (GDM) and screening for GDM, and of raised fasting plasma glucose (FPG) and clinical diagnosis of GDM, on the risk of late stillbirth.

Design: Prospective case-control study.

Setting: Forty-one maternity units in the UK.

The Association Between Childhood Leukemia and Population Mixing: An Artifact of Focusing on Clusters?

Background: Studies investigating the population-mixing hypothesis in childhood leukemia principally use two analytical approaches: (1) nonrandom selection of areas according to specific characteristics, followed by comparisons of their incidence of childhood leukemia with that expected based on the national average; and (2) regression analyses of region-wide data to identify characteristics associated with the incidence of childhood leukemia. These approaches have generated contradictory results. We compare these approaches using observed and simulated data.

Adjustment for time-invariant and time-varying confounders in 'unexplained residuals' models for longitudinal data within a causal framework and associated challenges.

'Unexplained residuals' models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework.

Survival, by Birth Weight and Gestational Age, in Individuals With Congenital Heart Disease: A Population-Based Study.

Background: Congenital heart disease (CHD) survival estimates are important to understand prognosis and evaluate health and social care needs. Few studies have reported CHD survival estimates according to maternal and fetal characteristics. This study aimed to identify predictors of CHD survival and report conditional survival estimates.

Risk estimates of recurrent congenital anomalies in the UK: a population-based register study.

Background: Recurrence risks for familial congenital anomalies in successive pregnancies are known, but this information for major structural anomalies is lacking. We estimated the absolute and relative risks of recurrent congenital anomaly in the second pregnancy for women with a history of a congenital anomaly in the first pregnancy, for all major anomaly groups and subtypes.

Methods: Population-based register data on 18,605 singleton pregnancies affected by major congenital anomaly occurring in 872,493 singleton stillbirths, live births and terminations of pregnancy for fetal anomaly were obtained from the Northern Congenital Abnormality Survey, North of England, UK, for 1985-2010.

Variation in health care for children and young people with cerebral palsies: a retrospective multicentre audit study.

Aim: To explore the provision and variations in care for children and young people with cerebral palsies (CP) registered with the population-based North of England Collaborative Cerebral Palsy Survey (NECCPS).

Method: This is a retrospective multicentre record audit of 389 children with CP (220 males, 148 females, 21 no data; median age at time of audit 12y 3mo), born between 1995 and 2002. Data were collected on cranial magnetic resonance imaging (MRI), hip and spine surveillance and management, and pain presence and management.

Langerhans cell histiocytosis in children with congenital anomalies: a population-based record linkage study.

Background: The etiology of Langerhans cell histiocytosis (LCH), a rare cancer-like disorder of the immune system, is largely unknown although a genetic component has been suggested based on familial cases, and reports of chromosome instability and genetic mutation. Associations between various cancers and congenital anomalies have been reported and although congenital anomalies have been noted in children with LCH only one study to date has reported their frequency. An association between congenital anomalies and LCH may suggest a common etiological pathway, in particular, a genetic pathway.