Rolling enrollment is a common method for participant recruitment in medical practice. In the longitudinal data, where researchers are often interested in outcomes occurring after a certain period of treatment, the definition of causal effects differs from that in the cross-sectional data. It poses new challenges for the application of matching methods in the longitudinal studies. Longitudinal matching is an extension of matching methods in longitudinal studies involving static interventions such as rolling enrollment. Currently, longitudinal matching methods are widely applied in the comparative effectiveness research. This article elucidates the fundamental principles, applicable conditions, code implementation, and application instances of four longitudinal matching methods through theoretical discussions and empirical illustrations. It provides methodological references for estimating causal effects in longitudinal data analysis.
Randomized controlled trials are considered as the gold standard for determining the causality, and are usually used to evaluate the efficacy and safety of medical interventions. However, in some cases it is not feasible to conduct a randomized controlled trial. In recent years, a framework called “target trial emulation study” has been formally established to guide the design and analysis of observational studies based on real-world data. This framework provides an effective method for causal inference based on observational studies. In order to facilitate domestic scholars to understand and apply the framework to solve related clinical problems, this article introduces it from the basic concept, framework structure and implementation steps, development status, and prospects.
