[R-sig-ME] R-sig-mixed-models Digest, Vol 71, Issue 34

[Laurence O'Dwyer]

Hi Alain,
             Thank you for the reply. As a follow-up, I have a
question or two relating to your pointers and suggestions.

>>
>> Hello to mixed-effects model experts,
>>
>>                                                         I am currently
>> trying to run an analysis on structural MRI data and would like to use
>> glmer or MCMCglmm to model my data. I have basic statistical knowledge and
>> would appreciate any guidance in the use of these R-tools from experts in
>> mixed effects models.
>>
>>                  In a crude way, I am interested in a model that might look
>> something like the following:
>>
>>
>>
>> moo = MCMCglmm(autism_spectrum_scores ~ Diagnosis + Striatum + Thalamus +
>> Amygdala + Hippocampus,
>>
>>                                            random = ~ADHD_symptom_scores
>>
>>                                                      + age
>>
>>                                                      + scanner_type
>>
>>                                                      + gender
>>
>>                                                      + total_brain_volume,
>>
>>                                            data=dat)
>>
>>
>
>
> Are you using gender as random effect?
>

Yes, I am using gender as a random variable, as there are differences
(although non-significant) in the ratio of Males:Females in each
experimental group.

>> It is a study of ADHD and autism. I have data for ~170 children with ADHD,
>> ~70 unaffected siblings, and ~80 controls - this is the fixed factor
>> "Diagnosis".
>>
>> I have the volumes of particular structures in the brain. These are the
>> fixed factors Striatum, Thalamus, etc. I am interested to know their
>> relationship with a scale of autistic traits (NOT ADHD traits) within all
>> experimental groups. For example, smaller volumes in the Striatum may be
>> associated with increased autistic traits.
>>
>> For the random effects, I want to control for differences in ADHD symptoms,
>> age, scanner type (two different scanners were used to collect the
>> volumetric data), gender and total brain volume.
>
> Yes you do. That is not a good idea. You may want to read a little bit
> on mixed modelling before doing this. Your model is overly complicated
> for 170 observations. I actually wonder whether this is mixed effects
> modelling; do you have multiple observations per child? If not...then it
> seems ordinary linear regression?


Sorry, I am not clear here on what you are referring to as "not a good idea"?
Each child underwent scanning. There are multiple observations (MRI
volumetrics,
as well as symptom counts from diagnostic questionnaires), for all children.
I felt mixed-effects models would be most effective and robust in this situation
as I am interested to know how the fixed effects influence the
response variable,
while controlling for a range of random effects that influence the
variance of the response.
This analysis also ties in with earlier work assessing the relationship between
autism scores and total brain white matter volume and total brain grey
matter volume,
for which mixed-effects models were quite informative. MCMC was used,
as the explanatory
variables are not normally distributed.


As suggested, I simplified the model and looked at the VIFs. I now
have 3 Fixed Effects with GVIFs:
Striatum    1.507092
Amygdala    1.281519
Hippocampus 1.557735


So, I would like to know if the resulting model could be considered
statistically sound, or if there are still gaping
holes in its statistical credibility:

try.5 = MCMCglmm(ASD_spectrum_VISK ~ Diagnosis_Simple + Striatum +
Amygdala + Hippocampus,
                                          random =
~Combined_Symptoms_inatt_plus_hyper
                                                    + age
                                                    + scanner_binary
                                                    + Gender
                                                    + TBV,
                                          data=dat)


This leads to a result that can be reasonably well interpreted
biologically and which is in line with
the study hypothesis: ADHD diagnosis has significant effect on the
autism score, and Striatal volume (p=0.078)
has a borderline significant effect on autism score.

I am particularly keen to know if my attempts to control for ADHD
symptoms, as well as age, scanner type, etc., are adequately
dealt with in the random effects section, or whether or not I need to
look into the specification of a prior which is noted in some of the
MCMCglmm documentation.

Any advice is greatly appreciated.

With thanks; Larry


>> A key point of the analysis would be to establish the relationship between
>> structural volumes and autistic scores, when levels of ADHD have been
>> controlled for.
>>
>> One problem is that all the structural volumes are closely correlated.
>> Previously, when working with two structural volumes that were correlated,
>> I used the regression residuals of one structural volume relative to the
>> other to isolate the unique contribution of each explanatory variable,
>> independent from what was shared between them. But, I don't think I can use
>> this approach with four structures that are highly correlated.
>>
>>                  There are probably many other statistical flies in the
>> ointment relating to the above. If anyone has any pointers as to how to
>> deal with the situation when multiple explanatory variables are correlated,
>
> dump some of them...after making scatterplots, and calculate VIF values.
> Or use them, and accept that SEs will be blown up.
>
> Kind regards,
>
> Alain
>> in a mixed-effects models framework, they would be appreciated.
>>
>>                  Thanks; Larry
>>