Causal Inference Analysis for Poorly Soluble Low Toxicity Particles, Lung Function, and Malignancy

Poorly soluble low toxicity particles such as carbon black and titanium dioxide have raised concern about possible nonmalignant and malignant pulmonary effects. This paper illustrates application of causal inference analysis to assessing these effects. A framework for analysis is created using directed acyclic graphs to define pathways from exposure to potential lung cancer or chronic airflow obstruction outcomes. Directed acyclic graphs define influences of confounders, backdoor pathways, and analytic models. Potential mechanistic pathways such as intermediate pulmonary inflammation are illustrated. An overview of available data for each of the inter-node links is presented. Individual empirical epidemiologic studies have limited ability to confirm mechanisms of potential causal relationships due to the complexity of causal pathways and the extended time course over which disease may develop. Therefore, an explicit conceptual and graphical framework to facilitate synthesizing data from several studies to consider pulmonary inflammation as a common pathway for both chronic airflow obstruction and lung cancer is suggested. These methods are useful to clarify potential bona fide and artifactual observed relationships. They also delineate variables which should be included in analytic models for single study data and biologically relevant variables unlikely to be available from a single study

Algorithm · Bioinformatics · Biology · Causal inference · Causal model · Computational biology · Confounding · Directed acyclic graph · Disease · Inference · Lung cancer · Pathology · Air Quality and Health Impacts · Computer Science · Health Systems, Economic Evaluations, Quality of Life · Medicine · Artificial Intelligence

• Causal Diagrams for Epidemiologic Research

  Sander Greenland, Judea Pearl et al.•Epidemiology•1999

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