Accounting for Comorbidity in Etiologic Research

Vahe Khachadourian,1 Magdalena Janecka1,2 1Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY, USA; 2Department of Population Health, NYU Grossman School of Medicine, New York, NY, USACorrespondence: Vahe Khachadourian, Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY, USA, Email [email protected]: Comorbidity between disorders is pervasive, and its relationship to the main conditions under investigation needs to be addressed for robust causal inference. However, many clinical etiologic studies still fail to capitalize on the theoretical advancements and improved recommendations regarding covariate adjustment in this context. Specifically, studies often lack explicit causal assumptions about the role of comorbidity in exposure–outcome relationships, potentially leading to inappropriate accounting for comorbid conditions and resulting in biased effect estimates. This study aims to explore common causal structures involving comorbidity and provide guidance for handling it in etiologic research.Methods: We use Directed Acyclic Graphs (DAGs) to depict six causal scenarios involving comorbidity as a confounder, mediator, collider, or consequence of the exposure or outcome, illustrated with real-world clinical examples. Simulations were conducted across 5,000 iterations for each scenario, assessing the impact of conditioning on comorbidity under four effect measures (risk difference, odds ratio, risk ratio, and mean difference). Bias was evaluated by comparing adjusted and unadjusted effect estimates to the true values.Results: The impact of conditioning on comorbidity varied by its causal role. Adjusting for comorbidity mitigated bias when it acted as a confounder but introduced bias when it was a mediator or collider. In instances where comorbidity was a consequence of either the exposure or outcome, the decision to adjust depended on the research objectives and could vary across effect measures.Discussion: Explicit causal assumptions are essential for selecting appropriate analytical strategies in etiologic research. This study provides practical guidance on analytical handling of the measures of comorbidity, highlighting the need for study design and analysis to align with research objectives. Future work should address more complex causal structures and other methodological challenges.Keywords: etiologic research, causal inference, comorbidity, epidemiology, simulation study