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Evidence triangulation may help identify the impact of study design elements on study findings and to tease out biased results when evaluating potential causal relationships; however, methods for triangulating epidemiologic evidence are evolving and have not been standardized. Building upon key principles of epidemiologic evidence triangulation and risk of bias assessment, and responding to the National Academies of Sciences, Engineering, and Medicine (NASEM) call for applied triangulation examples, the objective of this manuscript is to propose a triangulation framework and to apply it as an illustrative example to epidemiologic studies examining the possible relationship between occupational formaldehyde exposure and risk of myeloid leukemias (ML) including acute (AML) and chronic (CML) types. A nine-component triangulation framework for epidemiological evidence was developed incorporating study quality and ROB guidance from various federal health agencies (i.e., US EPA TSCA and NTP OHAT). Several components of the triangulation framework also drew from widely used epidemiological analytic tools such as stratified meta-analysis and sensitivity analysis. Regarding the applied example, fourteen studies were identified and assessed using the following primary study quality domains to explore potential key sources of bias: 1) study design and analysis; 2) study participation; 3) exposure assessment; 4) outcome assessment; and 5) potential confounding. Across studies, methodological limitations possibly contributing to biased results were observed within most domains. Interestingly, results from one study - often providing the largest and least-precise relative risk estimates, likely reflecting study biases, deviated from most primary study findings indicating no such associations. Triangulation of epidemiological evidence appears to be helpful in exploring inconsistent results for the identification of study results possibly reflecting various biases. Nonetheless, triangulation methodologies require additional development and application to real-world examples to enhance objectivity and reproducibility., Competing Interests: DJL, AJR, and HNL are employed by Stantec ChemRisk, a consulting firm that provides scientific support to the government, corporations, law firms, and various scientific/professional organizations. KAM and WJT are independent consultants who have provided scientific evaluation on behalf of clients in litigation and regulatory settings in the United States and Europe. HC has received funding for consulting projects related to formaldehyde. The content and the conclusions of the manuscript are exclusively those of the authors. This work was supported, in part, by funding from the Foundation for Chemistry Research and initiatives, a 501(c)(3) tax-exempt organization established by the American Chemistry Council., (© 2024 The Authors. Published by Elsevier Inc.)

Introduction: Causal epidemiology for regulatory risk analysis seeks to evaluate how removing or reducing exposures would change disease occurrence rates. We define interventional probability of causation (IPoC) as the change in probability of a disease (or other harm) occurring over a lifetime or other specified time interval that would be caused by a specified change in exposure, as predicted by a fully specified causal model. We define the closely related concept of causal assigned share (CAS) as the predicted fraction of disease risk that would be removed or prevented by a specified reduction in exposure, holding other variables fixed. Traditional approaches used to evaluate the preventable risk implications of epidemiological associations, including population attributable fraction (PAF) and the Bradford Hill considerations, cannot reveal whether removing a risk factor would reduce disease incidence. We argue that modern formal causal models coupled with causal artificial intelligence (CAI) and realistically partial and imperfect knowledge of underlying disease mechanisms, show great promise for determining and quantifying IPoC and CAS for exposures and diseases of practical interest., Methods: We briefly review key CAI concepts and terms and then apply them to define IPoC and CAS. We present steps to quantify IPoC using a fully specified causal Bayesian network (BN) model. Useful bounds for quantitative IPoC and CAS calculations are derived for a two-stage clonal expansion (TSCE) model for carcinogenesis and illustrated by applying them to benzene and formaldehyde based on available epidemiological and partial mechanistic evidence., Results: Causal BN models for benzene and risk of acute myeloid leukemia (AML) incorporating mechanistic, toxicological and epidemiological findings show that prolonged high-intensity exposure to benzene can increase risk of AML (IPoC of up to 7e-5, CAS of up to 54%). By contrast, no causal pathway leading from formaldehyde exposure to increased risk of AML was identified, consistent with much previous mechanistic, toxicological and epidemiological evidence; therefore, the IPoC and CAS for formaldehyde-induced AML are likely to be zero., Conclusion: We conclude that the IPoC approach can differentiate between likely and unlikely causal factors and can provide useful upper bounds for IPoC and CAS for some exposures and diseases of practical importance. For causal factors, IPoC can help to estimate the quantitative impacts on health risks of reducing exposures, even in situations where mechanistic evidence is realistically incomplete and individual-level exposure-response parameters are uncertain. This illustrates the strength that can be gained for causal inference by using causal models to generate testable hypotheses and then obtaining toxicological data to test the hypotheses implied by the models-and, where necessary, refine the models. This virtuous cycle provides additional insight into causal determinations that may not be available from weight-of-evidence considerations alone.