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Re: st: Multiple imputation for longitudinal data


From   Eduardo Nunez <enunezb@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Multiple imputation for longitudinal data
Date   Fri, 3 Dec 2010 15:52:26 -0500

This is a registry of patients with acute heart failure. Each visit
represent a hospital admission for dosease's decompensation (they are
not planned visits). Ptes are followed-up until death (or
lost-to-follow-up).
The degree of unbalance is due to: 1) It is an ongoing registry with
new patients recruited all the time, 2) High rate of mortality
(informative censoring), and 3) Lost to follow-up (the least
important).
We are measuring a continuous biomarker at each visit, and we are
planning to use jmre1 (package st0190 from
http://www.stata-journal.com/software/sj10-2) - Analyzing longitudinal
data in the presence of informative drop-out)
However, we had missing values on this biomarker as well as on some
the variables we will use as covariates, and this is why I am asking
for help to the list.
I think the informative censoring is taken care using "jmre1" routine,
which is what the authors claimed.

Thank you for helping me.

Eduardo



On Fri, Dec 3, 2010 at 12:30 PM, Austin Nichols <austinnichols@gmail.com> wrote:
> Eduardo Nunez <enunezb@gmail.com>:
> Can you tell us what kind of data these are?
> It looks like you have very severe attrition, and
> depending on the data you may want a hazard model,
> possibly assuming uninformative censoring,
> which might be simple to implement and require no imputation,
> or you may believe that censoring is informative,
> and either imputation or a hazard model would require untenable assumptions.
>
> 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?
>
>>> >>> 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
>>> >>>
>
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