Advanced Causal Inference Methods to Evaluate the Effectiveness of Cardiovascular Therapies

Cardiovascular disease (CVD) remains the leading global cause of death, despite the availability of effective preventive medications such as antihypertensives, statins, and antithrombotic agents. However, traditional observational studies often fail to establish causal relationships due to confounding and bias, limiting their ability to provide real-world evidence for clinical decision-making. Advanced causal inference methods—such as emulated target trials—offer a groundbreaking approach to evaluating medication effectiveness in real-world populations with greater validity and precision. This project will apply state-of-the-art causal inference techniques to large-scale healthcare data, generating robust evidence on the real-world effectiveness of medications for cardiovascular disease prevention. The findings will directly inform clinical guidelines and health policy, ensuring optimal medication use across diverse patient populations. Project Phases: The student will be part of the Big Data, Epidemiology, and Prevention Division within the Stroke and Ageing Research (STAR) group at Monash University, working on the following: Phase 1: Conduct a systematic review of existing studies using causal inference and emulated target trial methodologies in cardiovascular disease prevention. Phase 2: Design and implement an emulated target trial using large-scale linked healthcare data to assess the effectiveness of specific medications (e.g., antihypertensives, statins, antithrombotics) for CVD prevention. Phase 3: Apply and compare advanced causal inference techniques—such as inverse probability weighting and instrumental variable analysis—to improve the robustness of findings and address key methodological challenges. What the student will gain: With strong mentorship and interdisciplinary collaboration, the student will independently lead this cutting-edge research program while developing expertise in: ✅ Causal Inference & Pharmacoepidemiology – Mastering emulated target trials and advanced statistical techniques for real-world evidence generation. ✅ Big Data Analytics & Real-World Evidence – Using large-scale electronic health records and administrative datasets to evaluate cardiovascular medication effectiveness. ✅ Comparative Effectiveness & Precision Medicine – Identifying optimal preventive strategies tailored to different patient populations. ✅ Scientific Communication & Publication – Publishing high-impact research and contributing to global cardiovascular prevention guidelines. Research Training & Impact: Each phase will involve securing ethics approval (as required), refining research protocols with iterative feedback, collecting and analyzing data, and disseminating findings through high-impact publications. Upon successful completion of annual milestone reviews, the student will submit a PhD thesis by publication. This project provides a unique opportunity to leverage cutting-edge causal inference methodologies to generate real-world evidence, shaping the future of cardiovascular disease prevention and precision medicine.