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.

On improving the efficiency of trials via linear adjustment for a prognostic score

I’ve recently had the opportunity to spend a little time looking at an interesting approach for improving the efficiency of estimated treatment effects in clinical trials which exploits historical data. In this blog post I’ll give a few thoughts on the results in ‘Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score’, by Schuler et al 2022. The paper was published in the International Journal of Biostatistics, and an arXiv pre-print is available here.

Read more

G-formula for causal inference via multiple imputation

G-formula (sometimes known as G-computation) is an approach for estimating the causal effects of treatments or exposures which can vary over time and which are subject to time-varying confounding. It is one of the so called G-methods developed by Jamie Robins and co-workers. For a nice overview of these, I recommend this open access paper by Naimi et al 2017, and for more details, the What If book by HernĂ¡n and Robins. In this post, I’ll describe some recent work with Camila Olarte Parra and Rhian Daniel in which we have explored the use of multiple imputation methods and software as a route to implementing G-formula estimators.

Read more