Subgroup effects in antihypertensive and type 2 diabetes drug classes: a comparative effectiveness and safety study integrating real-world and genetic data
- Project No: iCase 2024/1
- Intake: 2024
Estimating subgroup treatment effects from clinical trials is notably difficult due to the limited sample size and under-representation of patients with multimorbidity. Much larger routinely collected observational data, equipped with bespoke designs and robust analytic approaches, can fill those critical evidence gaps and support more informed clinical decisions, ultimately improving patient outcomes, cutting costs, and boosting healthcare quality. The proposed project will apply existing and develop novel methods for the identification and characterisation of heterogeneity in safety and effectiveness of first- and second-line antihypertensive and antidiabetic drugs in subgroups of patients with varying clinical and genetic profiles.
Our project consists of four interconnected stages:
- To use advanced statistical and machine-learning techniques to stratify hypertension and T2D patients into subgroups, based on their clinical risk modelled from real-world data.
- To enrich these models with genetic predictors, including monogenic mutations or polygenic risk scores, utilizing the UK Biobank data.
- To generate estimates for predefined drug exposure-adverse outcome pairs within each subgroup in a systematic manner, while considering clinical risk, genetic risk, and a combination of both, within a causal inference framework. Methodologies include new-user cohort design, active-comparators, large-scale propensity score adjustment/matching, and negative control outcome experiments with empirical calibration.
- To triangulate the evidence of subgroup effects using novel genetic approaches such as Mendelian randomization.
- To repeat these analytic processes for drug exposure-therapeutic outcome pairs.
Execution and resources
This project will leverage data from UK's CPRD GOLD, Aurum, and UK Biobank, hosted in our servers at the University of Oxford, and additional international databases maintained by Janssen, a team experienced in phenotyping standardized concepts for the study of these drugs and related health outcomes.
Benefits of collaboration
This partnership offers a collaborative opportunity with profound implications for the treatment of hypertension and T2D. The Pharmacoepidemiology Research Group (Academic Partner) will gain access to international data sources and in-house expertise in data curation and processing, providing the candidate invaluable exposure to industry-led initiatives and working processes, such as the OHDSI network. Simultaneously, Janssen (Industry Partner) will tap into experience exchange in populating research, training opportunities in causal inference, and state-of-the-art expertise in analysing Biobank data, inherent in the Prof Prieto-Alhambra’s group.
The resultant findings of this project will yield advancements in personalized medicine for hypertension and T2D, contributing significantly to MRC's remit of improving human health through translational studies, and informing policy and practice both within the UK and in the global community.
Apply using course: DPhil in Clinical Epidemiology and Medical Statistics