We (Camila Olarte Parra (LSHTM), Rhian Daniel (Cardiff), myself, and David Wright (AstraZeneca)) recently put on arXiv a new paper which explores the use of estimators from both the causal inference and missing data literatures for estimating a so-called hypothetical estimand in a previously conducted clinical trial in diabetes.

# Estimands

These posts are on the topic of ‘estimands’ in clinical trials. The estimand defines how the treatment effect is quantified in the presence of so called intercurrent events in clinical trials. These are events which affect the existence or interpretation of patient outcomes after they occur.

## Is the ICH E9 estimand addendum compatible with model-based estimands?

Today I’m pleased to be giving a talk in Ghent as part of an afternoon of talks on the topic of estimands in trials. Treatment effects are often estimated in clinical trials using regression models for the outcome, with randomised treatment and often some other baseline variables as covariates. The coefficient of treatment is taken as the (estimate of) treatment effect. In my talk today I’ll be discussing whether the ICH E9 addendum on estimands is compatible with such effects or estimands, which I refer to as **model-based estimands**.

The slides can be viewed using the link below, but in a nutshell, my conclusion is that the addendum is not compatible with such estimands, because the addendum specifies that:

- The effect measure should be a population-level summary measure (suggesting, at least to me, things like means, medians, etc, not parameters in models)
- Definition of the estimand should come before specification of the statistical estimation method

Having drawn this tentative conclusion, I reflect on the pros and cons of model-based versus model-free estimands, in the specific context of randomised trials. Although we are very familiar with model-based estimands, I think there are strong reasons in favour of using **model-free estimands** in trials.

The slides can be viewed / downloaded using the links below.

## Causal (in)validity of the trimmed means estimand

This week I’ve been given the opportunity to present some ongoing work with colleagues Camila Olarte Parra and Rhian Daniel about the so called ‘trimmed means estimand’ in clinical trials at the International Biometric Conference in Riga, Latvia. The slides of my talk are available here for anyone interested. In this post I’ll give a brief overview of my talk.