Date:         Wed, 8 Jun 2011 12:26:51 -0400
Reply-To:     R B <ryan.andrew.black@gmail.com>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         R B <ryan.andrew.black@gmail.com>
Subject:      Re: Mixed Effects Model for group-randomised trial
In-Reply-To:  <1307484646426-4463258.post@n5.nabble.com>
Content-Type: text/plain; charset=ISO-8859-1

Still, Bruce, I think you raise an important point regarding an
UNstructured covariance type, particularly with regard to handling
repeated measures. I can't remember if there are more than two time
points and/or if the OP thinks the residual variance may be different
depending on the condition and/or time point. If yes to either of
these points, then he/she should consider replacing the second RANDOM
statement with a REPEATED statement and make the appropriate
specifications. Typically, the RANDOM intercept statement is
equivalent to a REPEATED statement with a compound symmetric (CS)
structure. If a CS structure is too restrictive, then a REPEATED
statement should be considered in place of the second RANDOM
statement.

Ryan

On Tue, Jun 7, 2011 at 6:10 PM, Bruce Weaver <bruce.weaver@hotmail.com> wrote:
> Absolutely right, Ryan.  Good catch.  If I'd looked at some of my own MIXED
> examples, I would have been reminded that I usually don't even bother
> including COVTYPE() when there is a random intercept only.
>
>
>
> R B wrote:
>>
>> Bruce,
>>
>> Generally speaking, there is no difference between the following two
>> RANDOM statements:
>>
>> /RANDOM=INTERCEPT | SUBJECT(Schoolnumb) COVTYPE(UN)
>> /RANDOM=INTERCEPT | SUBJECT(Schoolnumb) COVTYPE(VC)
>>
>> The UNstructured specification is not necessary if all that is being
>> estimated is a RANDOM intercept. Put another way, the intercept has
>> nothing with which to covary, so there is no need to specify a
>> covariance structure which allows for such covariance. Now, if we were
>> estimating both a RANDOM intercept and slope, such as:
>>
>> /RANDOM=INTERCEPT x | SUBJECT(Schoolnumb) COVTYPE(UN)
>>
>> then the UNstructured covariance matrix may be appropriate.
>>
>> Ryan
>>
>> On Tue, Jun 7, 2011 at 8:44 AM, Bruce Weaver
>> <bruce.weaver@hotmail.com> wrote:
>>> I have a couple comments/questions.
>>>
>>> 1. Why are you not including Sex as a factor in the model, especially
>>> since
>>> you are concerned about some imbalance?
>>>
>>> 2. Some authors (e.g. Twisk) recommend always starting with an
>>> unspecified
>>> covariance structure.  After inspecting the unconstrained covariance
>>> matrix
>>> this produces, you may be able to impose some other structure (which will
>>> save you some degrees of freedom).
>>>
>>> 3. Have you centered Age on some convenient in-range value (e.g., a value
>>> near the minimum, or the mean)?  This will make interpretation easier,
>>> particularly for the intercept.
>>>
>>>
>>> So I think your model would be something like:
>>>
>>> MIXED DV BY Condition Time Sex WITH Age
>>>  /FIXED=Condition Time Sex Age
>>>             Condition*Time Condition*Sex Time*Sex Condition*Time*Sex  |
>>> SSTYPE(3)
>>>  /METHOD=REML
>>>   /PRINT=SOLUTION TESTCOV
>>>  /RANDOM=INTERCEPT | SUBJECT(Schoolnumb) COVTYPE(UN)
>>>  /RANDOM=INTERCEPT | SUBJECT(Student*Schoolnumb) COVTYPE(UN)
>>>  /EMMEANS=TABLES(Condition)
>>>  /EMMEANS=TABLES(Time)
>>>  /EMMEANS=TABLES(Sex)
>>>  /EMMEANS=TABLES(Condition*Time)
>>>  /EMMEANS=TABLES(Condition*Sex)
>>>  /EMMEANS=TABLES(Time*Sex)
>>>  /EMMEANS=TABLES(Condition*Time*Sex)
>>> .
>>>
>>>
>>> HTH.
>>>
>>>
>>>
>>> christiane wrote:
>>>>
>>>> Thank you for replying. I am quite stuck with this one!
>>>>
>>>>
>>>> "Are the child and parent outcome variables the same?  If so, then child
>>>> v
>>>> parent is just another explanatory variable in the model, right?  If
>>>> not,
>>>> I think you'll need two models, one for children and one for parents."
>>>> I think I will need two models (one for outcomes rated by the parent,
>>>> and
>>>> one for outcomes rated by the child). I have measured outcomes with a
>>>> parent self report (e.g, Child Depression Inventory parent version) and
>>>> an
>>>> adolescent self-report version(e.g, Child Depression Inventory child
>>>> self-report version).
>>>>
>>>>
>>>> Yes,my primary interest is in Intervention vs Control, specifically in
>>>> whether the amount (or pattern) of change varies by Condition on my
>>>> outcome variables (e.g. child depression).
>>>>
>>>> It's a group randomised design, and unfortunately I do have a slight
>>>> selection bias (more distressed parents, but not children in the
>>>> intervention group).
>>>>
>>>> I hope this answers your clarification. Does my syntax get at all the
>>>> levels?
>>>>
>>>> Kind regards from Melbourne,
>>>>
>>>> Christiane
>>>>
>>>
>>>
>>> -----
>>> --
>>> Bruce Weaver
>>> bweaver@lakeheadu.ca
>>> http://sites.google.com/a/lakeheadu.ca/bweaver/
>>>
>>> "When all else fails, RTFM."
>>>
>>> NOTE: My Hotmail account is not monitored regularly.
>>> To send me an e-mail, please use the address shown above.
>>>
>>> --
>>> View this message in context:
>>> http://spssx-discussion.1045642.n5.nabble.com/Mixed-Effects-Model-for-group-randomised-trial-tp4447503p4461642.html
>>> Sent from the SPSSX Discussion mailing list archive at Nabble.com.
>>>
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>>
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>
>
> -----
> --
> Bruce Weaver
> bweaver@lakeheadu.ca
> http://sites.google.com/a/lakeheadu.ca/bweaver/
>
> "When all else fails, RTFM."
>
> NOTE: My Hotmail account is not monitored regularly.
> To send me an e-mail, please use the address shown above.
>
> --
> View this message in context: http://spssx-discussion.1045642.n5.nabble.com/Mixed-Effects-Model-for-group-randomised-trial-tp4447503p4463258.html
> Sent from the SPSSX Discussion mailing list archive at Nabble.com.
>
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