Prediction using Multiple Imputation
Calibration of generalized linear models and Cox regression models for prediction using
multiple imputation to account for missing values in the predictors as described in the paper by
"Mertens, Banzato and de Wreede" (2018) <1810.05099>. The methodology and calculations
described in this paper are fully implemented in this package. The vignette describes all data analytic steps
which allow users to replicate results using the package functions on the data analyzed in the paper or
on their own data.
Imputations are generated using the package 'mice' without using the outcomes of observations for which the
predictions are generated. Two options are provided to generate predictions. The first is prediction-averaging of
predictions calibrated from single models fitted on single imputed
datasets within a set of multiple imputations. The second is application of the Rubin's rules pooled model.
For both implementations, unobserved values in the predictor data of new observations for
which the predictions are derived are automatically imputed. The package contains two basic functions.
The first, mipred() generates predictions of outcome on new observations. The second, mipred.cv() generates
cross-validated predictions with the methodology on existing data for which outcomes
have already been observed. The present version is still in development and should support continuous,
binary and counting outcomes, but we have only thoroughly checked performance for binary outcome
logistic regression modeling. We will include the Cox regression extension later.1810.05099>