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.

What is meant by a ‘while on treatment’ estimand?

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The ICH E9 R1 addendum on estimands in clinical trials has made big waves in the clinical trial world in the last few years. It aims to provide a framework to think about and define more precisely what exactly the treatment effect(s) of interest is in a clinical trial, in light of what the addendum calls ‘intercurrent events’ (ICEs):

Events occurring after treatment initiation that affect either the interpretation or the existence of the
measurements associated with the clinical question of interest. It is necessary to address intercurrent
events when describing the clinical question of interest in order to precisely define the treatment effect
that is to be estimated.

A couple of weeks ago a really nice paper was published by Harrison and Brummel in the American Statistican which explored the five different ‘strategies’ described in the E9 addendum for handling ICEs in a simple example using potential outcomes. For each strategy they gave an example of an estimand defined using the strategy and a simple estimator for estimating the estimand from the data. In this post, I want to focus on the while on treatment strategy, as I think it’s one area where there is some debate as to what exactly the E9 addendum meant. I of course do not claim to have the definitive answer, but the following is my view.

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Estimating hypothetical estimands with causal inference and missing data estimators in a diabetes trial

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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.

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

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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.

bartlett_ghent_iche9_model_based_estimandsDownload