Following a recent request from someone, I’ve extended the functionality of my R package smcfcs, which performs multiple imputation of missing covariates, compatible with a user-specified substantive or outcome. The package can now impute compatibly with a flexible parametric Royston-Parmar type model. In this post I’ll briefly highlight some of the potential uses of this new functionality.
multiple imputation
Estimating hypothetical estimands with causal inference and missing data estimators in a diabetes trial
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.
Multiple imputation and its application – 2nd edition published
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).