Someone recently asked me about using substantive model compatible imputation, as implemented in smcfcs in R, to impute missing covariates, followed by fitting Fine and Gray models for the cumulative incidence functions using the crr function in the cmprsk package.
Missing data
Conference talk video – Bootstrap Inference for Multiple Imputation Under Uncongeniality and Misspecification
This week I was happy to give a talk at the online conference of the International Society for Clinical Biostatistics about my work with Rachael Hughes on different ways of combining bootstrapping with multiple imputation for missing data. For those who may be interested, the video of this is now available on YouTube. For further details of our work, please see the published paper.
Bootstrapping multiple imputation using multiple cores/processors in R
I’ve written previously about combining bootstrapping with multiple imputation, in particular when the imputation and analysis models may not be congenial. This work has recently been published in Statistical Methods in Medical Research (open access paper here). The approach we recommend in this paper, proposed earlier by Paul von Hippel, is implemented in the R package bootImpute.