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Re: st: Multiple imputation for longitudinal data


From   Clara Barata <maria_barata@mail.harvard.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Multiple imputation for longitudinal data
Date   Fri, 3 Dec 2010 14:34:01 -0500

Hi Eduardo,
A possible (albeit possibly limited) first approach would be to
arrange the data in wide rather than stacked format and impute that
way using Stata's MI. I think this is what Stas meant when he
mentioned the need to first -reshape- your data to make it one line
for each patient.
Clara


On Fri, Dec 3, 2010 at 12:20 PM, Eduardo Nunez <enunezb@gmail.com> wrote:
>
> Based on what you wrote, I imagine Stata hasn't implemented these
> methods ( that utilize the
> monotonicity of monotonicity).
> Would you guide me to the software that has these estimation methods.
> Do you know if it is implemented in R?
>
>
> Best regards,
>
> Eduardo
>
>
>
> On Fri, Dec 3, 2010 at 11:50 AM, Stas Kolenikov <skolenik@gmail.com> wrote:
> >
> > 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.
> >
> > *
> > *   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/

*
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