Clinical trials aim to draw conclusions about the effects of treatments, but many different questions could be investigated. For example, does the treatment work for only those patients who can tolerate the treatment and take as prescribed?, or does the treatment work for all patients even if not all treatment is received (e.g. not all doses of drug received)? Since different questions can lead to very different conclusions on treatment benefit, when planning a trial it is important to have a clear understanding of precisely what treatment effect the trial aims to investigate – this is what we call the ‘estimand’. The estimand captures for what patient population and under what conditions we seek to estimate a particular treatment effect. Using estimands helps to ensure trials are designed and analysed to answer the questions of interest to different stakeholders, including patients. We are conducting research to explore how to communicate about estimands with public partners’ and to obtain public partner perspectives on the importance of discussing estimands with public partners when designing a trial.

An important aspect of clinical trials is to collect data on any potential harmful effects, with the aim of ensuring that the benefit-risk balance is appropriate. The language used by trialists to describe these potential harmful effects is inconsistent. With potential benefits to be gained by harmonising language used to describe harmful effects we sought public opinion through a series of in-person focus groups. We also discussed how potential harmful effects are communicated in the scientific literature, as well as in public facing material on medications.This work will provide a starting point on preferred terminology by patients and the public to describe potential harmful intervention effect and highlight some key areas for improvement in public facing materials.

In this project we are working with members of the public with lived experience of using an app to help with their mental health to find the best way to use mental health apps that means patients will get the most benefit from them. The group has 5 members and meet every 6-12 months to discuss different aspect of the research such as looking at how activity in apps is currently described in research or thinking about the different ways in which activity within an app could be measured.

This activity is related to Jack's PhD work which looks at how should compliance be defined in smartphone app trials?

Ellie has formed a PPI group of 6 people to discuss the use of machine learning in healthcare research.

The group will be meeting seven times at 6-monthly intervals throughout Ellie’s doctoral fellowship. The first meeting will be introducing key concepts of machine learning and it’s existing uses in healthcare research. In the subsequent meetings, members will discuss how Ellie’s PhD research can and should be used ensure healthcare equity.