When a randomized controlled trial (RCT) is infeasible, unethical, or untimely, causal inference from observational data can serve as an effective alternative for scientific and clinical decision-making. However, observational studies harbor methodological vulnerabilities―not only confounding due to lack of randomization, but also selection bias or immortal time, arising from flawed study designs―that can fundamentally distort effect estimates. Target trial emulation has gained prominence as a framework for addressing these challenges. This approach has two steps：(1) specifying the protocol of a hypothetical pragmatic RCT (the target trial) that would answer the causal question of interest, and (2) explicitly emulating that trial with existing observational data. Its greatest contribution is the elimination of ambiguous causal questions in observational studies, transforming them into well-defined causal estimands. In this article, we synthesize the conceptual foundations of target trial emulation and detail methodological considerations for its implementation. As an illustrative example, we describe an observational study that compared denosumab with oral bisphosphonates for cardiovascular safety and fracture-prevention effectiveness in maintenance dialysis patients with osteoporosis. The target trial framework offers a structured approach that prevents design-induced biases and clarifies the limitations inherent in observational data, thereby enabling epidemiologists who grapple with causal questions to draw more valid inferences.

There is growing interest in generating evidence from routinely collected real-world data to support medical and regulatory decision-making. However, longitudinal study designs using real-world data are often complex, and text-only descriptions make it difficult for most readers to understand their designs. To address this issue, in 2019, experts from industry, government, and academia developed the “design diagram,” a framework for visualizing longitudinal study designs. The design diagram uses standardized terminology and a graphical structure to communicate study design details to readers, thereby improving reproducibility. Based on previous work by a joint task force between the International Society for Pharmacoepidemiology (ISPE) and the Professional Society for Health Economics and Outcomes Research (ISPOR), the diagram includes a comprehensive set of key study parameters related to reproducibility. It successfully presents study designs in an unambiguous and intuitive manner. Diagrams have been proposed for various study designs, including cohort, nested case-control, and self-controlled designs. Recently, a new diagram was developed that adds at-a-glance elements to show the observability of the source data used in the study. The use of design diagrams is recommended in both the ISPE/ISPOR-endorsed harmonized protocol template (HARPER) and in reporting guidelines for pharmacoepidemiological research, and its widespread use is expected. This paper describes the structure of the design diagram and provides examples of its use. Effective use of design diagrams is expected to improve the reproducibility and reliability of database studies.