Target Trial Emulation Applications in Hypertension Research: A scoping Review

ObjectivesTarget Trial Emulation (TTE) has emerged as a rigorous framework for causal inference using observational data, but its application in hypertension research remains underexplored. This scoping review aims to map current TTE applications, identify methodological strengths and weaknesses, and propose future directions for its use in hypertension research. Study Design and SettingWe performed a scoping review following the Joanna Briggs Institute (JBI) guidance and the PRISMA extension for Scoping Reviews (PRISMA-ScR) checklist. We searched multiple databases, and three independent reviewers conducted screening and extraction using Covidence review management software. 14 out of 1,352 articles met the inclusion criteria. ResultsMost studies used data from electronic health records, claims databases, and registries. All of the interventions were pharmacological except for one. Common confounding adjustment methods included inverse probability weighting (50%) and the g-formula (21.5%), complemented by regression-based models. However, time-varying confounders were inconsistently addressed, and loss to follow-up was often managed through simple censoring rather than statistical methods. Residual confounding remained a concern--although several studies acknowledged unobserved confounders, only five (36%) employed negative controls or e-values to assess their impact. While subgroup analyses were common, explicit heterogeneous treatment effect (HTE) estimation was limited. Advanced causal machine learning techniques for bias mitigation or HTE detection were not reported. ConclusionTTE shows strong potential to complement randomized controlled trials in hypertension research by providing more generalizable insights. While still in its early stages, current studies highlight its ability to address key challenges such as HTE, long-term outcomes, and dynamic treatment strategies.