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Re: st: Unbalanced repeated measures analysis question


From   K Jensen <k.x.jensen@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Unbalanced repeated measures analysis question
Date   Thu, 22 Jul 2010 16:50:09 +0100

I would like to just try to play around with it before I commit to a
book.  The repeated measure terms (MeasurerID, SubjectID) are not
nested. I think I would like to model with these as random effects and
measurer type as a fixed effect, having multiple measurements for each
and not being interested in individual subjects or measurers.

xi:xtmixed Result i.MeasurerType ||   SubjectID: || MeasurerID:

is *not* right for this, but I can't see any mention of non-nested
models in the help.

Thankyou

Karin

On 22 July 2010 14:35, Ploutz-Snyder, Robert (JSC-SK)[USRA]
<robert.ploutz-snyder-1@nasa.gov> wrote:
> Karin,
>
> You could define your model the way you suggested, yes, however
> mixed models can be specified a number of different ways
> depending on your research goals and how you want to consider the
> nesting of your repeated measures factors (i.e. random terms).
>
> There are a number of excellent books on this type of analysis,
> going by names including mixed-effects modeling, mixed modeling,
> higher level modeling (HLM), multi-level modeling (MLM) and
> probably a few other terms... If you are interested in a more
> Applied book that uses Stata in particular, Rabe-Hesketh and
> Skrondal put together a nice one book called Multilevel and
> Longitudinal Modeling Using Stata.  I think you might do well to
> take a course in MLM if you can to at least wrap your brain
> around the theory. But if you want to jump right in then a book
> like this one could get you going in the right direction.
>
> Rob
>
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of K Jensen
> Sent: Thursday, July 22, 2010 5:33 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Unbalanced repeated measures analysis question
>
> Thanks to Robert and David for your helpful comments. Sorry to sound
> stupid here but mixed models are entirely new to me. I have been
> reading up on them.
>
> I have the variables outlined below:
> SubjectID MeasurerID MeasurerType Result GoldStandard
> where MeasurerID is always a certain MeasurerType (1-3)
>
> SubjectID and MeasurerID should be random effects and MeasurerType
> fixed?  How would you specify that in the xtmixed syntax?  I am
> confused about having two grouping variables for the random effects.
>
> Karin
>
> On 21 July 2010 22:37, Ploutz-Snyder, Robert (JSC-SK)[USRA]
> <robert.ploutz-snyder-1@nasa.gov> wrote:
>> Karin,
>> I feel your pain RE Stata's anova syntax for repeated measures...
>>  But I also agree with David that I think your better bet is
>> probably to use -xtmixed- and then apply -margins- for  your
>> post-hoc comparisons, given the imbalance issue.  You can use
>> -margins- to compare each of the three measures to the gold
>> standard--akin to simple effect contrasts.
>>
>> If you wish to remain in the anova syntax, you might want to
>> check out the user written -anovalator- command, thanks to Phil
>> Ender from UCLA.  But from the sounds of your imbalanced design,
>> I would tend to lean more to -xtmixed- with -margins-
>>
>> (BTW--the Phil's website at UCLA has some nice walk-throughs
>> of all of this.)
>>
>> Rob
>>
>> -----Original Message-----
>> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Airey, David C
>> Sent: Wednesday, July 21, 2010 4:07 PM
>> To: statalist@hsphsun2.harvard.edu
>> Subject: re: st: Unbalanced repeated measures analysis question
>>
>>
>> I think when you have comparisons to a gold standard, or all
>> comparisons with one control, that there are specific ANOVA
>> post-hoc tests that perform better than all possible or all
>> pairwise comparisons procedures.
>>
>> There is the complication that you are testing for equivalence,
>> as you say.
>>
>> The Stata command -xtmixed- can do what -anova- can. Sometimes
>> -manova- or -mvtest- is useful with repeated measures too.
>>
>> It is hard to understand how your design is unbalanced without
>> seeing the data cross-tabs, etc.
>>
>>> Hi
>>>
>>> I have data on measuring a biological property for three different
>>> methods plus a gold standard. Different people were trained in each
>>> method (1,2 or 3) and measured the same subjects during different
>>> sessions, together with the gold standard measurement.
>>>
>>> So the data look like
>>> SubjectID MeasurerID MeasurerType Result GoldStandard  Diff
>>> 1         1          1            95     99            -4
>>> 1         2          3            102    99            +3
>>> 1         3          2            92     99            -7
>>> ...
>>> 1        10          3            105    99            +6
>>> 2         1          3             98   100            -2
>>> ...
>>>
>>> Sometimes patients would be called in to see the consultant and so
>>> missed for a particular measurer, but otherwise all the measurers
>>> would measure all the patients seen in a particular session. Different
>>> sets of measurers (but all trained by methods 1,2 or 3) were used on
>>> each session (individual measurers 1-10 on session 1, 11-20 on session
>>> 2 etc).
>>>
>>> The gold standard measurements on each session are roughly normally
>>> distributed, as are the differences from the gold standard. We are
>>> interested in the accuracy of each of the three methods.
>>>
>>> Is it OK to do some sort of repeated measures ANOVA here, with an
>>> unbalanced design? If it is what would be the syntax (Stata 10)? Sorry
>>> to sound pathetic but I just can't get the anova command with the
>>> repeated option to work here.
>>>
>>> Is there a better measure to use than the difference to reflect the
>>> fact that we are interested in a comparison with a gold standard?
>>>
>>> Thankyou
>>> Karin
>
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