25,099 research outputs found
JointAI: Joint Analysis and Imputation of Incomplete Data in R
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 and extensions thereof
as well as survival models and joint models for longitudinal and survival data,
that take arguments analogous to 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.Comment: imputation, Bayesian, missing covariates, non-linear, interaction,
multi-level, survival, joint model R, JAG
A shared-parameter continuous-time hidden Markov and survival model for longitudinal data with informative dropout
A shared-parameter approach for jointly modeling longitudinal and survival data is proposed. With respect to available approaches, it allows for time-varying random effects that affect both the longitudinal and the survival processes. The distribution of these random effects is modeled according to a continuous-time hidden Markov chain so that transitions may occur at any time point. For maximum likelihood estimation, we propose an algorithm based on a discretization of time until censoring in an arbitrary number of time windows. The observed information matrix is used to obtain standard errors. We illustrate the approach by simulation, even with respect to the effect of the number of time windows on the precision of the estimates, and by an application to data about patients suffering from mildly dilated cardiomyopathy
A Review on Joint Models in Biometrical Research
In some fields of biometrical research joint modelling of longitudinal measures and event time data has become very popular. This article reviews the work in that area of recent fruitful research by classifying approaches on joint models in three categories: approaches with focus on serial trends, approaches with focus on event time data and approaches with equal focus on both outcomes. Typically longitudinal measures and event time data are modelled jointly by introducing shared random effects or by considering conditional distributions together with marginal distributions. We present the approaches in an uniform nomenclature, comment on sub-models applied to longitudinal measures and event time data outcomes individually and exemplify applications in biometrical research
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