[R-sig-ME] treating measurement occasions as a numerical or as a factor predictor

[K Imran M]

Dear Ben,

Your answer has given me a new perspective at looking at my data
analysis again. At least I am happy that I have treated the
measurement occasions as categorical. This is a small longitudinal
data on patients after acute stroke attacks. We would like to have
more measurement occasions (waves) but were limited by our resources.

Many thanks

Regards,

Kamarul



On Sun, Dec 13, 2015 at 3:03 AM, Ben Bolker <bbolker at gmail.com> wrote:
> On 15-12-12 11:48 AM, Rich Shepard wrote:
>> On Sun, 13 Dec 2015, K Imran M wrote:
>>
>>> I would like to ask a question about treating measurement occasions in a
>>> longitudinal analysis specifically when using linear mixed model. In my
>>> study, I have taken data on 3 separate occasions (at baseline, at 1 month
>>> and at 3 months post baseline). I am not sure what is best approach treat
>>> these measurement occasions in my analysis using lmer or lme functions.
>>> Should I treat them as a numeric or as a factor variable. My feeling says
>>> that I should treat such measurement occasions as a factor but I do not
>>> have strong theoretical reasons for that.
>>
>> Kamarul,
>>
>>   What question do you want to answer with your data?
>>
>> Rich
>>
>
>    A few thoughts to consider:
>
> * with only 3 measurement occasions you won't really be able to treat
> them as a random effect (not enough distinct levels to estimate
> among-occasion variance reliably)
> * if you treat measurement occasion as numeric (i.e., a linear effect of
> time) you will assume that the change per month is identical throughout
> the observation period (i.e. you will expect 2 times as much change from
> 1 month to 3 months post baseline as from baseline to 1 month post baseline)
> * if you treat measurement occasion as categorical (factor) you will
> estimate 2 parameters for the effect of occasion rather than 1.  There
> are various ways you can break this up, depending on the contrasts you
> choose (default 'contr.treatment': baseline vs 1 month, baseline vs. 3
> months.  MASS::contr.sdif() gives you successive differences, making the
> variable an ordered factor gives you contr.poly() (linear, quadratic
> contrasts) by default.
>
>    I would *generally* say that you're not complicating the model
> much/spending very many parameters(degrees of freedom) by using a
> categorical rather than a numeric input for time, and otherwise you're
> making a fairly strong assumption, so I would recommend categorical.
>
>   Ben Bolker
>
>



-- 
Dr. Kamarul Imran Musa (MD MCommunityMed)
Associate Professor (Epidemiology and Biostatistics) &
Public Health Physician,
Dept of Community Medicine,
School of Medical Sciences,
Universiti Sains Malaysia,
16150 Kbg Kerian Kelantan
MALAYSIA

ResearcherID: http://www.researcherid.com/rid/N-3198-2015
Google-scholar: 'Kamarul Imran Musa'  at https://goo.gl/D3o3y6
ORCID ID: orcid.org/0000-0002-3708-0628
ScopusID: 18634847200

Personal blog: http://designdataanalysis.wordpress.com
Email : drki.musa at gmail.com , k.musa at lancaster.ac.uk