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.
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.
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.
Comments on FDA guidance ‘Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products’
The FDA recently published revised guidance on statistical methods for adjusting for baseline covariates in trials. Overall I like the guidance and think it will prove useful. In this post I’ll give a few thoughts on aspects of the revised guidance, organised according to the sections of the guidance document.