Directed acyclic graphs (DAGs) are essential tools based on prior assumptions, capable of clearly depicting causal relationships between variables, aiding in the identification of confounders and assessment of bias. They have been widely applied in the field of epidemiology. However, the construction of DAGs is prone to the subjectivity of researchers. This paper interprets the DAGs construction and application guidelines from the BMJ and elaborates on the seven steps for building DAGs: clarifying the research question and target population, identifying relevant variables, reaching consensus and pre-registering, determining data collection or datasets based on the consensus graph, selecting analysis methods and variable measurement, conducting sensitivity analysis, and reporting the DAGs. By following these steps, researchers can construct DAGs more scientifically, identify the minimal sufficient adjustment set, and enhance the scientific validity and reliability of their studies. Despite its advantages, DAGs have limitations. For instance, failure to consider all relevant factors may lead to unexplained disturbances in the analysis results or incorrect causal inferences, thus failing to verify other key assumptions in causal reasoning. Therefore, it is recommended to integrate DAGs with data-driven methods to maximize their strengths and compensate for their shortcomings.
Patient reported outcome measures (PROM) are widely used in clinical research and practice. To aid the interpretation of PROM, researchers have proposed the minimal important difference (MID), the smallest change or difference that patients perceive as important. However, the estimation methods of MID are numerous and inconsistent, which brings difficulties to selecting the optimal MID estimate to interpret PROM results. To address this issue, a research team from McMaster University in Canada has proposed an approach for selecting the optimal MID. This method includes three core steps: evaluating the credibility of MID estimates, assessing the consistency among credible MID estimates, and selecting the optimal value based on contextual factors. The credibility evaluation instrument for anchor-based MID examines five core criteria, including the data sources of PROM and anchor, the interpretability of anchor, the correlation between anchor and PROM, the precision of MID estimates, and the judgment of anchor thresholds. When there are multiple credible MID estimates, the optimal MID estimate is selected by evaluating the consistency among the estimates and considering contextual factors that affect the variability among the estimates, such as the type of intervention, follow-up time, and disease severity. In addition, the team provided recommendations to improve the reporting quality of MID studies. This article provides a detailed introduction and interpretation of these developments, aiming to enhance researchers' and clinicians' understanding and application of MID, thereby supporting clinical research and healthcare decision-making.