JointAI: Joint Analysis and Imputation of Incomplete Data in R
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by
Nicole S. Erler, Dimitris Rizopoulos, Emmanuel M. E. H. Lesaffre
2019
Abstract
Missing data occur in many types of studies and typically complicate the
analysis. Multiple imputation, either using joint modelling or the more
flexible fully conditional specification approach, are popular and work well in
standard settings. In settings involving non-linear associations or
interactions, however, incompatibility of the imputation model with the
analysis model is an issue often resulting in bias. Similarly, complex outcomes
such as longitudinal or survival outcomes cannot be adequately handled by
standard implementations. In this paper, we introduce the R package JointAI,
which utilizes the Bayesian framework to perform simultaneous analysis and
imputation in regression models with incomplete covariates. Using a fully
Bayesian joint modelling approach it overcomes the issue of uncongeniality
while retaining the attractive flexibility of fully conditional specification
multiple imputation by specifying the joint distribution of analysis and
imputation models as a sequence of univariate models that can be adapted to the
type of variable. JointAI provides functions for Bayesian inference with
generalized linear and generalized linear mixed models as well as survival
models, that take arguments analogous to their corresponding and well known
complete data versions from base R and other packages. Usage and features of
JointAI are described and illustrated using various examples and the
theoretical background is outlined.
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