multiple imputation for longitudinal data in R?

[sara jambarsang]

Hi all, I am trying to find R package for multiple imputation in longitudinal data, Does anyone know this?

--

Sara Jambarsang
Ph.D.student in Biostatistics

[Steve Simon, P.Mean Consulting]

On 9/30/2013 4:15 AM, sara jambarsang wrote:

> Hi all, I am trying to find R package for multiple imputation in
> longitudinal data, Does anyone know this?

There's a package with the delightful acronym of MICE (Multiple
Imputation by Chained Expectations) that I've used. Imputation for
longitudinal models (with missingness for the outcome at some of the
time points in addition to missingness for some of the covariates) can
be tricky from a variety of perspectives, so proceed with caution.

Steve Simon, n...@pmean.com, Standard Disclaimer.
Sign up for the Monthly Mean, the newsletter that
dares to call itself average at www.pmean.com/news

[Pedro Emmanuel Alvarenga Americano do Brasil]

Statmasters,

When i needed some sofisticated imputation a while a go i used mice. However at the time i looked around at r site search and there was at least three other packages with some imputation function. And if you are patient enough and look around for non cran packages you will be able to find some others with imputation functions such as zelig. Unfortunately, i cant remember  right now something specific for time dependent information or longitudinal data. Indeed this can be tricky depending on what data is missing and what mechanism of missingness is involved.

Pedro Brasil
via Android (:)=

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[SR Millis]

 1. J Clin Epidemiol. 2013 Sep;66(9):1022-8. doi: 10.1016/j.jclinepi.2013.03.017.

Epub 2013 Jun 21.

Multiple imputation of missing values was not necessary before performing a

longitudinal mixed-model analysis.

Twisk J, de Boer M, de Vente W, Heymans M.

Department of Epidemiology and Biostatistics, VU University Medical Centre, de

Boelelaan 1118, Amsterdam 1081 HV, The Netherlands; Department of Health

Sciences, Section Methodology and Applied Biostatistics, VU University, de

Boelelaan 1085, Amsterdam 1081 HV, The Netherlands. Electronic address:

jwr....@vumc.nl.

BACKGROUND AND OBJECTIVES: As a result of the development of sophisticated

techniques, such as multiple imputation, the interest in handling missing data in

longitudinal studies has increased enormously in past years. Within the field of 

longitudinal data analysis, there is a current debate on whether it is necessary 

to use multiple imputations before performing a mixed-model analysis to analyze

the longitudinal data. In the current study this necessity is evaluated.

STUDY DESIGN AND SETTING: The results of mixed-model analyses with and without

multiple imputation were compared with each other. Four data sets with missing

values were created-one data set with missing completely at random, two data sets

with missing at random, and one data set with missing not at random). In all data

sets, the relationship between a continuous outcome variable and two different

covariates were analyzed: a time-independent dichotomous covariate and a

time-dependent continuous covariate.

RESULTS: Although for all types of missing data, the results of the mixed-model

analysis with or without multiple imputations were slightly different, they were 

not in favor of one of the two approaches. In addition, repeating the multiple

imputations 100 times showed that the results of the mixed-model analysis with

multiple imputation were quite unstable.

CONCLUSION: It is not necessary to handle missing data using multiple imputations

before performing a mixed-model analysis on longitudinal data.

Copyright © 2013 Elsevier Inc. All rights reserved.

PMID: 23790725  [PubMed - in process]

~~~~~~~~~~~
Scott R Millis, PhD, ABPP, CStat, PStat®
Board Certified in Clinical Neuropsychology, Clinical Psychology, & Rehabilitation Psychology 
Professor
Wayne State University School of Medicine
Email: aa3...@wayne.edu
Email: srmi...@yahoo.com
Tel: 313-993-8085

---

From: Pedro Emmanuel Alvarenga Americano do Brasil <emmanue...@gmail.com>
To: meds...@googlegroups.com
Cc: sara jambarsang <s.jamb...@gmail.com>
Sent: Tuesday, October 1, 2013 8:52 AM
Subject: Re: {MEDSTATS} multiple imputation for longitudinal data in R?

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[Adrian Sayers]

One of the hardest thing of mi in longitudinal data is using a method which preserves the dependency within the data.

http://www.missingdata.org.uk/

Is a very useful site with links to packages that specifically aimed at mi in longitudinal data.

also a new program has a template for doing this type of analysis automatically.

http://www.bristol.ac.uk/cmm/news/2012/29.html

bw
A