[R-sig-ME] Imputation methods mixed model analysis

Sorkin, John j@ork|n @end|ng |rom @om@um@ry|@nd@edu
Thu Apr 16 17:17:12 CEST 2020

MICE is an algorithm that allows imputation of data. Rather than using the same strategy to impute data, MICE allows selection of  imputation methods that are most appropriate for the datum being imputed; one variable might be imputed using linear regression, another using logistic regression, etc.
Generally one imputes data using a program (or function) that is designed for imputation and then analyzes the data using a program (or function) that is designed specifically to analyze the data. By separating imputation from analysis, once one learns imputation, one can use imputation for any data set, and needs not learn the ins and outs of a specific analysis' imputation technique.

John David Sorkin M.D., Ph.D.
Professor of Medicine
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology and Geriatric Medicine
Baltimore VA Medical Center
10 North Greene Street
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)

From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> on behalf of Breugelmans, S. (Sara) <S.Breugelmans using student.ru.nl>
Sent: Thursday, April 16, 2020 9:33 AM
To: r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org>
Subject: [R-sig-ME] Imputation methods mixed model analysis

Dear colleagues,

For my thesis I am conducting a mixed model analysis on longitudinal data. However, when I tried to run an intercept-only model I got the following error:

preCORT_ICC <- lmer(Data_F_long$Cortisol_pre ~ 1 + (1 | Data_F_long$ID), data = Data_F_long)
Error in KhatriRao(sm, t(mm)) : (p <- ncol(X)) == ncol(Y) is not TRUE


When I searched this error I saw that it might have to do something with the number of NA's. So then I thought it would be better to use some kind of imputation strategy.

I was wondering if there is a build-in function in lmer() to do this. Or is it better to manually impute the data before analysing.

I already found a function called mice(). Does anyone of you have experience with mice() and would you recommend using it?

Of course it would be more convenient if I could use some kind of build-in function in lmer().

Thank you for your response!

Kind regards


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