Abstract

Trial emulation, also known as target trial emulation, has significantly advanced epidemiology and causal inference by providing a robust framework for deriving causal relationships from observational data. This approach aims to reduce biases and confounding factors inherent in observational studies, thereby improving the validity of causal inferences. By designing observational studies to mimic randomized controlled trials (RCTs) as closely as possible, researchers can better control for confounding and bias. Key components of trial emulation include defining a clear time-zero, simulating random assignment using techniques like propensity score matching and inverse probability treatment weighting, assessing group comparability by standardized mean differences and establishing a clear comparison strategy. The increasing availability of large-scale real-world databases, such as research cohorts, patient registries, and hospital records, has driven the popularity of target trial emulation. These data sources offer information on patient outcomes, treatment patterns, and disease progression in real-world settings. By applying the principles of target trial emulation to these rich data sources, researchers can design studies that provide robust causal inferences about the effects of interventions, informing clinical guidelines and regulatory decisions. Despite its advantages, trial emulation faces challenges like data quality, unmeasured confounding, and implementation complexity. Future directions include integrating trial emulation with machine learning techniques and developing methods to address unmeasured confounding. Overall, trial emulation represents a significant advancement in epidemiology, offering a valuable tool for deriving accurate and reliable causal inferences from observational data, ultimately improving public health outcomes.

Graphical abstract