Understanding ethnic differences in the comparative effectiveness of antidiabetic medications using high-dimensional propensity scores in electronic

Introduction
There are enormous inequalities in rates of type 2 diabetes across ethnic groups in the UK. Population of South Asian and African descent develop diabetes earlier in life and progress more quickly to complications, such as stroke and heart disease.

A potential cause of ethnic disparities in diabetes outcomes arises because therapeutic guidelines are predominantly derived from white European populations. UK guidelines currently recommend a range of second-line diabetes treatments due to clinical equipoise. It is unknown whether treatment response differs by ethnicity. Differences in the effectiveness of medications may arise from biological factors to cultural acceptability of different medications.

Project description
In this project I will compare the effectiveness of antidiabetic medications at preventing complications between ethnic groups with the same class of antidiabetic medication. I also plan to use patterns in prescription data to study adherence to medications and determinants of non-adherence among patients from different ethnic groups. I will then use data from the UK biobank to study genetic susceptibility and drug response to type 2 diabetes across different ethnic groups..

One disadvantage of using electronic health records to study treatment affects is the inability to control which patients are assigned to which treatment groups. In clinical trials patients are often randomly assigned to treatment groups to ensure that the groups being compared are as similar. This makes it easier to isolate the effects of the treatment. In reality, there are non-random differences between treatment groups because patients and healthcare providers choose which treatment is most suitable.

I will use high-dimensional propensity scoring to control for differences in the treatment groups. This methods uses an algorithm to identify potentially confounding variables and then creates comparison groups among which these variables are balanced. This make it easier to isolate the effects of the exposure (medication combined with ethnicity) on the outcome (vascular complications of diabetes).

Relevance
This project will make a valuable contribution to the evidence base for ethnic differences in the effectiveness of second-line diabetes treatment. The findings may feed into the development targeted treatment regimens that will be more effective at preventing vascular complications for type 2 diabetes patients from different ethnic groups.

MRC strategy and core skills
This studentship meets the MRC skills priority of enhancing quantitative skills. Within this remit, I will gain skills and understanding of:

- Different ways of assessing the effectiveness of treatments and commonly used statistical techniques in pharmacoepidemiology.
- Triangulation between different quantitative data sources, including genomic and health utilization data.
- Computational skills required to managing and analyse big data.
- The application of complex quantitative techniques to control for confounding in observational datasets.
Keywords: diabetes, ethnicity, electronic health records, pharmacoepidemiology, high dimensional propensity scoring.