[R-sig-ME] Random intercept/slopes on two correlated outcomes

[Houslay, Tom]

Hi Theodore, just in case it's of interest, there is another option - ASReml (commercial software from VSNi) can fit the type of model you are looking for, with more complex variance structures (and it has an R interface). This thread from the forum might be informative in terms of how to set up such a model:

http://www.vsni.co.uk/forum/viewtopic.php?t=1202

Cheers (and good luck!)

Tom

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From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> on behalf of r-sig-mixed-models-request at r-project.org <r-sig-mixed-models-request at r-project.org>
Sent: 01 February 2017 09:40
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Subject: R-sig-mixed-models Digest, Vol 122, Issue 1



Date: Wed, 01 Feb 2017 10:37:44 +0200
From: Theodore Lytras <thlytras at gmail.com>
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Random intercept/slopes on two correlated outcomes
Message-ID: <3197712.2r4pMF0lQW at equinox2>
Content-Type: text/plain; charset="us-ascii"

Hi all,

I have repeated measures on individuals, and I'm fitting two LMMs with random
intercepts and slopes per participant, on two outcomes (Y1, Y2) as follows:

library(lme4)
m1 <- lmer(Y1 ~ age + X + (age | id), data=dat)
m2 <- lmer(Y2 ~ age + X + (age | id), data=dat)

Fixed covariates for the two outcomes are the same, and id = participant ID.

However, my two continuous outcomes Y1 and Y2 are correlated (highly), thus I
would like to jointly model them (including estimating their correlation).

What is the appropriate way to do so in this case? Can lme4 do it, or do I
have to resort to MCMCglmm or JAGS? Could someone point me in the right
direction (for either lme4, MCMCglmm or JAGS), including any helpful papers,
guides, etc ??

Thank you,

Theodore Lytras

Epidemiologist, PhD student
Hellenic Centre for Disease Control and Prevention




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