Presentation (CTI)

OBJECTIVES: Causal inference and directed acyclic graphs (DAGs) are powerful tools in pharmaceutical research, facilitating analysis of critical questions and informing decision-making. By visualizing causal relationships, DAGs help identify confounders, mediators, and colliders, thus guiding the selection of covariates to control confounding and improve causal estimates. They also play a pivotal role in detecting and mitigating biases, including selection, measurement, and collider biases, thereby improving study design and analysis robustness. For comparative effectiveness research, DAGs clarify causal pathways, supporting the evaluation of treatment effects and real-world evidence for drug efficacy and safety. Additionally, they inform and help optimize study designs for both randomized controlled trials and observational studies. DAGs support risk assessments by analyzing real-world data for adverse drug reactions and long-term safety. They also support data integration and evidence synthesis by identifying compatible datasets and combining findings while preserving causal interpretability. Beyond analysis, DAGs serve as effective educational tools for communicating complex causal relationships and informing regulatory decisions, drug labeling, and healthcare policies.
METHODS: Specifically in regards to bias detection, DAGs provide a clear framework for visualizing relationships among variables to assess potential sources of bias in causal inference. Bias typically arises from three sources: the data source (e.g., systematic inclusion/exclusion of subjects or stakeholder influence), study design, and data analysis methods. While SAS procedures like PROC ASSESSBIAS address biases from analysis methods, they overlook biases from data sources and study design. DAGs bridge this gap by identifying biases from all sources, offering a wider understanding of causal inference.
RESULTS: SAS Viya, SAS 9, R, and Python offer strengths and limitations for conducting causal inference and leveraging DAGs in pharmaceutical research. We explore these comparative strength and limitations throughout this work.
CONCLUSIONS: Standardized tools for constructing DAGs would ensure consistency in analysis, enabling reproducible and comparable results across studies.