DOI: 10.25934/pr00006115
title: Real-world treatment effectiveness of novel anti-diabetic agents in people with type 2 diabetes: Maximising the applicability of clinical trials

description: To get the best healthcare members of the public need their doctors and other clinical staff to know whether new treatments work for them. For example, there are several new classes (types) of drug treatment for diabetes, but which of these are most effective in real-world settings is uncertain. This work sets out to determine which of three new drug classes for type 2 diabetes are most effective in real-world settings. Around 6% of the population of the UK has diabetes (with similar proportions in the US and Europe) this uncertainty is therefore an issue for a large number of people.

The best way to know whether a new medicine works is to perform a randomised clinical trial. In clinical trials, participants are selected to have different medicines at random (i.e. by chance). As a result, participants receiving different treatments are, on average, similar.  Consequently, researchers can compare groups receiving different treatments, and decide which are the most effective. However, trial participants are commonly younger and fitter than other patients. As a result, clinicians and others have expressed uncertainty as to whether results from trials are relevant to many patients in "real-world" settings.

To address this concern, some researchers have instead studied treatments using "routine data". Unlike with trials, routine data can be collected from all patients receiving healthcare (e.g. from medical records) including many older frailer patients.

However, when this kind of data is used to compare treatments it is very difficult to be sure (even with very sophisticated statistical methods) that any differences have been caused by the medication. This is because, unlike in trials, different treatments are often offered (or not offered) to patients because of differences in their clinical features.

In this project, we propose to overcome the weaknesses of clinical trials and of routine data by using the strengths of both. We will use routine data to "calibrate" trial results. When trial results are calibrated, findings from under-represented groups (e.g. older women) influence the overall results more than findings from other over-represented groups (e.g. younger men). After calibration we can be more confident that the trial results are relevant. Calibration does not "break" the randomness of trials; calibrated results remain reliable.

Some older calibration methods required researchers to have access to very detailed results from every relevant trial (e.g. the result for every trial participant). In most situations, this meant calibration was unfeasible. However, we recently developed a method to perform calibration which does not require this level of detail for every trial. This calibration is feasible for many more conditions and treatments. We now propose, for the first time, to use this new method to calibrate trials using routine data. Specifically, we will perform the calibration to decide which of the newer diabetes medicines are most effective in real-world patients in Scotland, England, and China. We will obtain routine data from a complete register of people with diabetes in Scotland, a routine data registry in England and from two hospitals in China. Having identified a group of patients suitable for treatment with the newer medicines, we will calculate the likely benefits and harms of each of the newer medicines as if the original clinical trials had been conducted in China, in Scotland or in England.

We will produce an overall summary result from all the trials, making this available to clinicians and people with diabetes. Since around 6% of the population of the UK has diabetes (with similar proportions in the US and Europe) this has the potential to benefit a large number of people.

We will also feed the results into a health economic model to predict the likely costs, benefits, and value for money. Such models are used by organisations such as  the National Institute for Health and Care Excellence (NICE) to inform guidelines and regulations about medicines.

To better communicate our findings about the effectiveness and value for money of each medicine, we will develop an interactive web app, designed to be used by researchers, clinicians, and people with diabetes. It will allow users to compare results which have been obtained the conventional way, alongside results obtained using calibration.
This project will produce results about differences in the effectiveness of newer drugs for diabetes that are reliable, and that clinicians can confidently apply to patients with diabetes in the real-world settings studied. As well producing tangible benefits for people with diabetes, this will also demonstrate methodologically, for the first time, that calibration can improve the relevance of trial results.