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Re: st: Multiple imputation for longitudinal data


From   Stas Kolenikov <skolenik@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Multiple imputation for longitudinal data
Date   Fri, 3 Dec 2010 10:50:47 -0600

What I am saying is that there are estimation methods that utilize the
monotonicity of monotonicity. Of course you can the existing methods
to produce something sensible. The monotone options, however, are
designed to work across the data set, not along the data set, so you
would want to -reshape- your data to make it one line for each
patient.

On Fri, Dec 3, 2010 at 10:34 AM, Eduardo Nunez <enunezb@gmail.com> wrote:
> Thank you, Stas.
> I can handle the monotone missing pattern either using ICE or MI
> impute while specifying the monotone option.
> But my question goes further on how to account for clustering on
> patient ID (which is the cluster unit)?.
> Should I impute data separately for each patient? or include pteID
> variable in the imputation model?
>
> I appreciate any help.
>
> Eduardo
>
>
>
>
> On Thu, Dec 2, 2010 at 6:39 PM, Stas Kolenikov <skolenik@gmail.com> wrote:
>> You have monotone missing data, and you would most likely be better
>> off utilizing the methods for monotone missing data rather than
>> bluntly rely on multiple imputation. Check Little and Rubin's book on
>> missing data, chapter 7 (in the 2nd edition).
>>
>> On Thu, Dec 2, 2010 at 5:11 PM, Eduardo Nunez <enunezb@gmail.com> wrote:
>>> Dear Statalisters,
>>>
>>> I have Stata 11.1 (MP - Parallel Edition).
>>>
>>> I am interested in performing multiple imputation on a longitudinal
>>> data (on several variables with a percent of missing between 1-15%),
>>> were subjects are the cluster units with few observations in time.
>>> See below the data structure:
>>>
>>> xtdes, pattern(1000)
>>>
>>>     pid:  1, 2, ..., 1438                                   n =       1432
>>>   visit:  1, 2, ..., 12                                     T =         12
>>>           Delta(visit) = 1 unit
>>>           Span(visit)  = 12 periods
>>>           (pid*visit uniquely identifies each observation)
>>>
>>> Distribution of T_i:   min      5%     25%       50%       75%     95%     max
>>>                         1       1       1         2         3       6      12
>>>
>>>     Freq.  Percent    Cum. |  Pattern
>>>  ---------------------------+--------------
>>>      650     45.39   45.39 |  1...........
>>>      359     25.07   70.46 |  11..........
>>>      202     14.11   84.57 |  111.........
>>>       91      6.35   90.92 |  1111........
>>>       52      3.63   94.55 |  11111.......
>>>       44      3.07   97.63 |  111111......
>>>       11      0.77   98.39 |  1111111.....
>>>        9      0.63   99.02 |  11111111....
>>>        6      0.42   99.44 |  111111111...
>>>        4      0.28   99.72 |  1111111111..
>>>        3      0.21   99.93 |  11111111111.
>>>        1      0.07  100.00 |  111111111111
>>>  ---------------------------+--------------
>>>     1432    100.00         |  XXXXXXXXXXXX
>>>
>>> The article included in Stata FAQ ("How can I account for clustering
>>> when creating imputations with mi impute?") suggested using a
>>> "multivariate
>>> normal model to impute all clusters simultaneously" or strategy 3,
>>> although mentioned that is best suited to balanced repeated-measures
>>> data.
>>>
>>> Clearly, my data is not balanced. Moreover, the percent of data
>>> missing increased as patient follow-up gets far from baseline.
>>>
>>> Is there any other method suited for this type of longitudinal data?
>>> If not, how stringent is the limitation of not being balanced.
>>>
>>> Please, any help is welcome!
>>>
>>>
>>> Eduardo
>>> *
>>> *   For searches and help try:
>>> *   http://www.stata.com/help.cgi?search
>>> *   http://www.stata.com/support/statalist/faq
>>> *   http://www.ats.ucla.edu/stat/stata/
>>>
>>
>>
>>
>> --
>> Stas Kolenikov, also found at http://stas.kolenikov.name
>> Small print: I use this email account for mailing lists only.
>>
>> *
>> *   For searches and help try:
>> *   http://www.stata.com/help.cgi?search
>> *   http://www.stata.com/support/statalist/faq
>> *   http://www.ats.ucla.edu/stat/stata/
>>
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>



-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.

*
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*   http://www.ats.ucla.edu/stat/stata/


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