CAUsal Inference Methods to Inform MedicineS ReguLation and Guidance: CAUSAL

Research vision My vision is to is to reduce harmful use of medicines globally.

I will apply my expertise spanning sophisticated statistical analyses of health data, clinical pharmacology and medicines governance, to lead and develop a platform for application of causal inference to population-scale data, to generate evidence to inform safer and more equitable medicines use. Background Randomised Controlled Trials (RCT) are the 'gold standard' in medicines approval.

However, their relevance to medicines in wider populations is limited in some cases, for example where the RCT has narrow inclusion criteria; or where certain groups are not represented in the RCT. In these the lack of 'gold-standard' evidence can lead to individuals receiving inappropriate medicines.

Health-data driven causal inferences approaches can now be used within observational data to give evidence where RCTs are not possible, or to enrich RCT data, to inform clinical decisions.

This is possible due to availability of national level data (~57 million) accessed through Trusted Research Environments in the UK.

Objectives To deliver this research I will meet the following objectives: WP 1 Develop data infrastructure to understand where and how medicines are used, and where there is inequity of use. Obj1.

Link medicines data to health outcomes in the UK through Trusted Research Environments and international Electronic Health data. Obj 2. Integrate cohort data where granular disease data or genomic data can enhance analyses. Obj 3. Build new datasets where key medicines or safety outcomes are missing.

WP 2 Develop and test methods of causal inference in large scale Electronic Health Data to inform safe, effective, inclusive and equitable use of medicines. Obj 4.

Use causal inference methods to generate evidence on safety and efficacious use of medicines in populations excluded from trials or where trials are not possible. WP3 Simulate the impact of results of causal inference for a) health outcomes b) economic impacts. Obj 5. Evaluate the health and economic outputs of use cases to demonstrate the utility to policy makers and payers.

WP4 Translation of evidence to practice Obj 6.

Variation in medicines use can impact equity and safety; investigate how medicines governance at a systems level in the UK could reduce this variation. Obj 7. Create a network of investigators and policy makers to ensure high quality methods are developed to use alongside RCTs.

The specific questions we address in WP2 (Obj 4) will be informed by a cross-sector committee, but will span from: using observational data to gathering evidence for use of medicines in a wider population than an RCT, to understanding the potential for repurposing similar drugs based on their mechanism of action.

Anticipated impact By working with researchers, regulators, patients, the public and practitioners in the UK and internationally, I will ensure that my team apply the best methodology, that our outputs are integrated into clinical guidelines rapidly, and that the questions we address are relevant.

This will give clinicians confidence due to evidence-based prescribing, and improve medicines safety for patient benefit.