Background Directed acyclic graphs (DAGs) are popular tools for identifying appropriate adjustment strategies for epidemiological analysis. However, a lack of direction on how to build them is problematic. As a solution, we propose using a combination of evidence synthesis strategies and causal inference principles to integrate the DAG-building exercise within the review stages of research projects. We demonstrate this idea by introducing a novel protocol: ‘Evidence Synthesis for Constructing Directed Acyclic Graphs’ (ESC-DAGs)’. Methods ESC-DAGs operates on empirical studies identified by a literature search, ideally a novel systematic review or review of systematic reviews. It involves three key stages: (i) the conclusions of each study are ‘mapped’ into a DAG; (ii) the causal structures in these DAGs are systematically assessed using several causal inference principles and are corrected accordingly; (iii) the resulting DAGs are then synthesised into one or more ‘integrated DAGs’. This demonstration article didactically applies ESC-DAGs to the literature on parental influences on offspring alcohol use during adolescence. Conclusions ESC-DAGs is a practical, systematic and transparent approach for developing DAGs from background knowledge. These DAGs can then direct primary data analysis and DAG-based sensitivity analysis. ESC-DAGs has a modular design to allow researchers who are experienced DAG users to both use and improve upon the approach. It is also accessible to researchers with limited experience of DAGs or evidence synthesis.

Elucidating the factors that contribute to healthy ageing is an important research goal. Physical activity (PA) has been associated with benefits for cognitive function (CF). However, most of this evidence comes from longitudinal cohort studies which, in the absence of experimental design, have limited scope to make causal inferences regarding observed relationships. This review aimed to utilise recent methodological developments allowing researchers to formulate and answer stronger causal questions using observational data, by following a best-practice method for synthesising evidence to produce a graphical causal model known as a directed acyclic graph (DAG). Following a search of 3 databases (EMBASE, MEDLINE and PsycINFO), 21 observational studies on the PA-CF relationship were reviewed and their methodological quality, characteristics, and key findings were summarised. The outcomes of interest were the covariates and modelling practices employed in each study. The reported covariates were synthesised against a set of criteria to determine their role in the DAG as confounders or mediators of the PA-CF relationship. Every included study had some areas of methodological weakness. The resulting DAG included a wide range of biopsychosocial covariates spanning the entire life-course and indicated potential intermediate pathways between PA and CF via structural brain health. Strengths, limitations and implications of this review for modelling decisions are discussed, prior to the model being taken forward to inform an empirical analysis using data from the UK Biobank cohort, separate from this review.

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