ESTIMANDS & ESTIMATION

Estimands & Estimation Within Clinical Trials

Ensuring trials answer the questions of interest: Implementation of the estimand framework

Team: Suzie Cro, Victoria Cornelius, Brennan Kahan, Ian White, James Carpenter, Richard Emsley, Beatriz Goulao

When evaluating the effect of a treatment in a clinical trial, different questions can be addressed. For example, does the treatment work when it is received as prescribed?, or does the treatment work regardless of whether all is received? The answers to these questions may lead to different conclusions on treatment benefit. It is therefore important to have a clear understanding of exactly what treatment effect a trial intends to demonstrate, referred to as the ‘estimand’. Trial design, conduct and analysis can then be aligned to address this. There is a need to implement the estimand framework introduced in the ICH E9 (R1) addendum across clinical trial units.

Our aim is to show trialists how to use the estimand framework by providing implementation tools. This to increase uptake of the framework, to ensure trials are designed and analysed to answer the questions of interest.

We have reviewed current practice on using estimands in trials and have developed a workshop for clinical trial statisticians and clinicians on the implementation of the framework, supported by the MRC NIHR TMRP. A second workshop, also supported by the MRC NIHR TMRP, provides guidance on Methods of Analysis for Different Estimands (MADE).

Estimation methods for treatment policy strategies with missing data

Team: Suzie Cro, Ian White, Matteo Quartagno, James Roger, James Carpenter

A treatment policy strategy if often used to handle intercurrent events such as treatment withdrawal. This entails targeting the effect of the intervention under study regardless of any treatment withdrawal. Estimation requires the collection of outcome data following treatment withdrawal, but data is often missing after treatment withdrawal complicating the analysis. Any analysis of a trial with missing data requires an untestable assumption about the missing data, which inference critically then depends on. Efficient and unbiased methods that make contextually relevant and plausible assumptions for the missing data are required to alleviate this problem.

We are developing novel multiple imputation methods for use in this setting. This includes the recently introduced retrieved dropout reference-base centred multiple imputation and further development of reference-based multiple imputation methods.

How should compliance be defined in smartphone app trials?

Team: Jack Elkes, Suzie Cro, Victoria Cornelius

The need to validate the use of smartphone apps and other digital technologies in healthcare is rapidly growing. A randomised controlled trial remains the gold standard approach and typically, an intention-to-treat analysis will be performed to determine if the intervention is beneficial.

However, the use of an app is known to decline substantially over time and an additional question of interest to answer is how effective the app in those is who use it (‘comply’). Unlike drug and behavioural interventions, compliance with digital interventions is more complex to define. There are currently recommended approaches to define participant compliance for smartphone app use in a trial. This is needed when calculating the benefit of treatment receipt (using complier causal inference methods).

We are conducting research to identify the different ways patients access the app based on key metrics; duration in app, pages accessed, and time of day accessed. PCA will be used to identify clusters of the different user profiles, which in turn will help us to develop a strategy for defining compliance to an app.