2,038 research outputs found
Method and data evaluation at NASA endocrine laboratory
The biomedical data of the astronauts on Skylab 3 were analyzed to evaluate the univariate statistical methods for comparing endocrine series experiments in relation to other medical experiments. It was found that an information storage and retrieval system was needed to facilitate statistical analyses
MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors
In this paper we describe MIDAS: a SAS macro for multiple imputation using distance aided selection of donors which implements an iterative predictive mean matching hot-deck for imputing missing data. This is a flexible multiple imputation approach that can handle data in a variety of formats: continuous, ordinal, and scaled. Because the imputation models are implicit, it is not necessary to specify a parametric distribution for each variable to be imputed. MIDAS also allows the user to address the sensitivity of their inferences to different assumptions concerning the missing data mechanism. An example using MIDAS to impute missing data is presented and MIDAS is compared to existing missing data software.
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
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