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.

Hypothetical estimands – a unification of causal inference and missing data methods

Camila Olarte Parra, Rhian Daniel and myself have just released a pre-print on arXiv (now published in Statistics in Biopharmaceutical Research) in detailing recent work looking at statistical methods targeting so called hypothetical estimands in clinical trials. The ICH E9 addendum on estimands is having a widespread impact on the way clinical trials are planned and analysed. One of the strategies described by the addendum for handling so called intercurrent events is the hypothetical strategy. This is where one hypothesizes of a way in which the trial could be modified such that the intercurrent event in question would not take place. For example, in trials where patients may receive a rescue medication, we could conceive of a trial where such medication were not made available. The goal of inference is then what treatment effect we would have seen in such a modified trial.

In the paper, building on work by others (e.g. Lipkovich et al 2020), we show how causal inference concepts and methods can be used to define and estimate hypothetical estimands. Currently estimation of estimands which use the hypothetical strategy is predominantly carried out using missing data methods such as mixed models and multiple imputation. To do so, any outcome measurements available after the intercurrent event being dealt with using the hypothetical strategy are deleted/ignored, and an analysis using these methods is performed, assuming the resulting missing data are missing at random (MAR). We set out to see how estimation of hypothetical estimands would proceed using the language and machinery from causal inference.

In this post I’ll highlight a few of the things the paper covers.

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