[R-sig-ME] model specification for repeated measure

[Dot Dumuid]

I'm sorry to come back to this question (see below for original
question)... but I have one more thing to ask.

Because I am *not* interested in whether mental health *changes* between
the two time points, but I am only interested in whether physical activity
predicts mental health (when controlling for other things like general
health and income), is it true that I don't put "time" in the model as a
fixed effect?

By using a random intercept for ID, the model already caters for the fact
that observations are matched by ID.

So my model could just be:

model <- lmer (MH ~ PA + GH + INC + (1|participant.ID) )

(Assuming, off course, no assumptions are violated,...and I am ignoring
random slopes at the moment)
Then I can predict MH for various min/day of PA (e.g., mean baseline PA,
mean baseline PA+10, mean baseline PA+20... etc), keeping GH and INC
constant. Subtracting the new predicted MHs from the mean predicted MH
should tell me how MH is predicted to change when PA changes across the
retirement transition.

Does this sound OK?
Thank you,
Dot


(This is the original question....)

*Dear mixed model experts,*
*We have a dataset of older adults. We measured their mental health (MH) 6
months before retirement and again 12 months post retirement.*
*At both of these time points we also measured their physical activity (PA)
(min/day), income (INC) and general health (GH).*
*We would like to create a model that tells us if change in physical
activity over the retirement threshold predicts change in mental health,
and we'd like to use the model to predict how much mental health is
predicted to change when physical activity is increased from perhaps 15
minutes to 60 minutes. We'd like to use a mixed model rather than just
using change (difference) scores. And we'd like to control for things like
change in general physical health and change in income.*

*This is what the data look like*

*ID  time  MH    PA    GH    INC*
*01  pre     4      15     56     560*
*02  pre     5      30     30    1200*
*..    .....     ..       ..       ..        ...*
*01  post   7      40     50      50*
*02  post   8      45     30      0 *

*I'm not sure how best to build the model. Something like this?*

*model <- lmer (MH ~ PA * time + GH + INC + (1|participant.ID) )*

*Thank you in advance.*



On Fri, Feb 2, 2018 at 6:22 AM, Dot Dumuid <haveaballphysio at gmail.com>
wrote:

> Thanks for the suggestions. I greatly appreciate you taking the time, and
> I look forward to trying out the ideas.
> Thanks, Dot
>
> On Fri, Feb 2, 2018 at 5:53 AM, Malcolm Fairbrother <
> malcolm.fairbrother at umu.se> wrote:
>
>> Hi Dot,
>>
>> This specification would yield a single coefficient for the
>> between-individual and within-individual effects. That is, you’re assuming
>> the association is the same over time as it is across individuals at a
>> single point in time. I wouldn’t expect this to be a safe assumption, and
>> there’s a pretty straightforward fix: centre your time-varying predictors
>> by their mean for each person. That will yield within effects equivalent to
>> what you’d get from a fixed effects model.
>>
>> For more information about this, see:
>> https://www.cambridge.org/core/services/aop-cambridge-core/
>> content/view/S2049847014000077
>> and/or
>> https://www.researchgate.net/publication/299604336_Fixed_and
>> _Random_effects_models_making_an_informed_choice
>>
>> Hope that’s useful,
>> Malcolm
>>
>>
>> Malcolm Fairbrother
>> Professor of Sociology
>> Umeå University <http://www.umu.se/english>
>> Sweden
>>
>>
>>
>> Date: Thu, 1 Feb 2018 21:07:10 +1030
>> From: Dot Dumuid <haveaballphysio at gmail.com>
>> To: r-sig-mixed-models at r-project.org
>> Subject: [R-sig-ME] model specification for repeated measure
>>
>> Dear mixed model experts,
>> We have a dataset of older adults. We measured their mental health (MH) 6
>> months before retirement and again 12 months post retirement.
>> At both of these time points we also measured their physical activity (PA)
>> (min/day), income (INC) and general health (GH).
>> We would like to create a model that tells us if change in physical
>> activity over the retirement threshold predicts change in mental health,
>> and we'd like to use the model to predict how much mental health is
>> predicted to change when physical activity is increased from perhaps 15
>> minutes to 60 minutes. We'd like to use a mixed model rather than just
>> using change (difference) scores. And we'd like to control for things like
>> change in general physical health and change in income.
>>
>> This is what the data look like
>>
>> *ID  time  MH    PA    GH    INC*
>> 01  pre     4      15     56     560
>> 02  pre     5      30     30    1200
>> ..    .....     ..       ..       ..        ...
>> 01  post   7      40     50      50
>> 02  post   8      45     30      0
>>
>> I'm not sure how best to build the model. Something like this?
>>
>> model <- lmer (MH ~ PA * time + GH + INC + (1|participant.ID) )
>>
>> Thank you in advance.
>> Dot
>>
>>
>>
>

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