ointAI: Joint Analysis and Imputation of Incomplete Data in R

ointAI: Joint Analysis and Imputation of Incomplete Data in R

: imputation, Bayesian, missing covariate, nonlinear, interaction, multi-level, survival, joint model, R, JAG

Missing data occur in many types of studies and typically complicate the analysis.
Multiple imputation, either using joint modeling or the more flexible fully conditional
specification approach, are popular and work well in standard settings. In settings involving nonlinear 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 modeling 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 and extensions thereof as well as survival models and
joint models for longitudinal and survival data, that take arguments analogous to the
corresponding well known functions for the analysis of complete data from base R and
other packages. Usage and features of JointAI are described and illustrated using various
examples and the theoretical background is outlined.

Nicole S. Erler

https://www.jstatsoft.org/article/view/v100i20

Erasmus Medical Center

November 2021

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English

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