Applying Evidence Synthesis for Constructing Directed Acyclic Graphs to Identify Causal Pathways Affecting U.S. Early-Stage Non-Small Cell Lung Cancer Treatment Receipt and Overall Survival

Background/Objectives: Directed acyclic graphs (DAGs) inform the epidemiologic statistical modeling confounders to determine close to true causal relationships in a study context. They inform the inclusion of the predictive model variables that affect the causal relationship. Non-small cell lung cancer (NSCLC) is frequently diagnosed, aggressive, and the second leading cause of cancer deaths in the United States. Determining factors affecting both the guideline-concordant treatment receipt and survival outcomes for early-stage lung cancer will help inform future statistical models aiming to achieve a close to true causal relationship. Methods: Peer-reviewed original research published during 2002–2023 was identified through PubMed, Embase, Web of Sciences, Clinical trials registry, and the gray literature. DAGitty version 3.1, an online software program, developed implied DAGs and integrated DAG graphics. The evidence synthesis for constructing directed acyclic graphs (ESC-DAGs) protocol was utilized to guide DAG development. The conceptual models utilized were Andersen and Aday for factors affecting treatment receipt and Shi and Steven for survival outcome factors. Results: A total of 36 studies were included in the DAG synthesis out of 9421 retrieved across databases. Eight studies served in the synthesis of treatment receipt DAG, while 28 studies were used for the survival outcomes DAG. There were 10 causal paths and 13 covariates for treatment receipt and 2 causal pathways and 32 covariates for survival outcomes. Conclusions: There are very few studies reporting on factors affecting early-stage NSCLC guideline-concordant care receipt compared to factors affecting its survival outcomes in the past two decades of original research. Future investigations can utilize data extracted in the current study to develop a meta-analysis informing effect size.