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.
I am delighted to write this blog post announcing the publication of the second edition of the book ‘Multiple Imputation and its Application’, published by Wiley, and which I am a co-author along with colleagues James Carpenter, Tim Morris, Angela Wood, Matteo Quartagno, and Mike Kenward.
Key additions in the second edition are:
- in depth discussion of congeniality and compatibility, and the practical implications of the theory for these for data analysts
- an updated chapter on performing imputation with derived variables, such as interactions, non-linear effects, sum scores, splines
- expanded chapter on MI with survival data, including imputing missing covariates in Cox models and MI for case-cohort and nested case-control studies
- new chapters on multiple imputation for / in the context of:
- prognostic models
- measurement error and misclassification
- causal inference
- using MI in practice
- practical and theoretical exercises in each chapter
We hope it will be useful for those handling missing data by multiple imputation in their analyses, particularly in regards to thinking about how to use it in a way which accommodates the various complexities that are often present in statistical analyses.
The book should now be available “in all good bookshops”, as they say. You can find it at Amazon (please note I may receive a commission if you subsequently purchase from Amazon after clicking this link).
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.