scholarly journals Directed acyclic graphs to explore causality in Epidemiological study designs, part I: an introduction to DAGs

Qeios ◽  
2020 ◽  
Author(s):  
Arindam Basu
Keyword(s):  
Epidemiological Study ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs ◽  
Study Designs

scholarly journals K-02 The evolution of occupational epidemiology

2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A1.2-A1
Author(s):  
Neil Pearce
Keyword(s):  
Missing Values ◽  
Occupational Exposures ◽  
Directed Acyclic Graphs ◽  
Work Organisation ◽  
Occupational Epidemiology ◽  
Acyclic Graphs ◽  
Musculoskeletal Problems ◽  
Causes Of Disease ◽  
Inference Methods ◽  
Study Designs

In this talk, I review the evolution of occupational epidemiology over the last 40 years. Methodologically, the field is almost unrecognizable compared to what was ‘standard practice’ 40 years ago. Methodological changes include the use of new study design and statistical methods, such as counterfactual theory, directed acyclic graphs (DAGs), IPW, g-estimation, g-computation, multiple imputation for missing values, sensitivity analysis, and bootstrapping. Biomarkers and various molecular and omics measures are increasingly used for exposure assessment, and exploration of mechanisms. The exposures and outcomes under study have also evolved, e.g. with increased consideration of psychosocial factors, work organisation, musculoskeletal problems, mental health and neurological disease. Despite all of these changes, many of the fundamentals of occupational epidemiology remain the same. The discovery of new causes of occupational disease continues to be lead by astute observers (including astute clinicians and astute workers), rather than by ‘bigdata’ or ‘omics’ methods. The strategy for investigation of possible occupational causes of disease continues to require a variety of study designs and approaches, including ‘descriptive’ studies, and triangulation across study designs and populations (albeit while utilising new molecular biology and statistical techniques). The causal assessment of occupational exposures and their health effects continues to require a wide variety of types of evidence in humans and animals, as well as mechanistic evidence. Forty years later, the Bradford-Hill considerations have been augmented but not been replaced, and the IARC ‘rules’ for combining various types of evidence remain the state-of-the-art. Reports of the death of ‘traditional epidemiology’ (and its replacement by ‘modern epidemiology’ and ‘causal inference’ methods) have been exaggerated.

scholarly journals Using compartmental models to simulate directed acyclic graphs to explore competing causal mechanisms underlying epidemiological study data

2020 ◽  
Vol 17 (167) ◽  
pp. 20190675
Author(s):  
Joshua Havumaki ◽  
Marisa C. Eisenberg
Keyword(s):  
Epidemiological Study ◽  
Study Design ◽  
Simulated Data ◽  
Population Level ◽  
Obesity Paradox ◽  
Directed Acyclic Graphs ◽  
Compartmental Models ◽  
Causal Mechanisms ◽  
Acyclic Graphs ◽  
Study Results

Accurately estimating the effect of an exposure on an outcome requires understanding how variables relevant to a study question are causally related to each other. Directed acyclic graphs (DAGs) are used in epidemiology to understand causal processes and determine appropriate statistical approaches to obtain unbiased measures of effect. Compartmental models (CMs) are also used to represent different causal mechanisms, by depicting flows between disease states on the population level. In this paper, we extend a mapping between DAGs and CMs to show how DAG-derived CMs can be used to compare competing causal mechanisms by simulating epidemiological studies and conducting statistical analyses on the simulated data. Through this framework, we can evaluate how robust simulated epidemiological study results are to different biases in study design and underlying causal mechanisms. As a case study, we simulated a longitudinal cohort study to examine the obesity paradox: the apparent protective effect of obesity on mortality among diabetic ever-smokers, but not among diabetic never-smokers. Our simulations illustrate how study design bias (e.g. reverse causation), can lead to the obesity paradox. Ultimately, we show the utility of transforming DAGs into in silico laboratories within which researchers can systematically evaluate bias, and inform analyses and study design.

scholarly journals Mathematical modeling of directed acyclic graphs to explore competing causal mechanisms underlying epidemiological study data

2019 ◽  
Author(s):  
Joshua Havumaki ◽  
Marisa C. Eisenberg
Keyword(s):  
Epidemiological Study ◽  
Study Design ◽  
Simulated Data ◽  
Population Level ◽  
Obesity Paradox ◽  
Epidemiological Studies ◽  
Directed Acyclic Graphs ◽  
Causal Mechanisms ◽  
Acyclic Graphs ◽  
Study Results

1AbstractAccurately estimating the effect of an exposure on an outcome requires understanding how variables relevant to a study question are causally related to each other. Directed acyclic graphs (DAGs) are used in epidemiology to understand causal processes and determine appropriate statistical approaches to obtain unbiased measures of effect. Compartmental models (CMs) are also used to represent different causal mechanisms, by depicting flows between disease states on the population level. In this paper, we extend a mapping between DAGs and CMs to show how DAG–derived CMs can be used to compare competing causal mechanisms by simulating epidemiological studies and conducting statistical analyses on the simulated data. Through this framework, we can evaluate how robust simulated epidemiological study results are to different biases in study design and underlying causal mechanisms. As a case study, we simulated a longitudinal cohort study to examine the obesity paradox: the apparent protective effect of obesity on mortality among diabetic ever-smokers, but not among diabetic never-smokers. Our simulations illustrate how study design bias (e.g., reverse causation), can lead to the obesity paradox. Ultimately, we show the utility of transforming DAGs into in silico laboratories within which researchers can systematically evaluate bias, and inform analyses and study design.

scholarly journals Directed acyclic graphs: An under-utilized tool for child maltreatment research

2019 ◽  
Vol 91 ◽  
pp. 78-87 ◽  
Author(s):  
Anna E. Austin ◽  
Tania A. Desrosiers ◽  
Meghan E. Shanahan
Keyword(s):  
Child Maltreatment ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs
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