Target trial emulation in hypertension research: a scoping review of current applications and methodological practices

Target trial emulation (TTE) is a demanding framework for causal inference using observational data, yet its use in hypertension research is limited. This scoping review maps current TTE applications, highlights methodological strengths and gaps, and suggests future directions. Following Joanna Briggs Institute (JBI) and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, we screened multiple databases and included 14 of 1352 articles. Common methods for confounding adjustment included inverse probability weighting (50%) and the g-formula (21.5%), often alongside regression models. Time-varying confounders were inconsistently addressed, and loss to follow-up was typically handled via simple censoring. Residual confounding remained a concern; although unobserved confounders were noted, only five studies (36%) used negative controls or e-values. Subgroup analyses were frequent, but causal machine learning for heterogeneous treatment effect (HTE) estimation was not reported. Although still in its early stages, TTE in hypertension research shows promise for addressing key challenges, including HTE, long-term outcomes, and dynamic treatment strategies.