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Re: st: Multiple imputation for longitudinal data
From
Eduardo Nunez <[email protected]>
To
[email protected]
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 <[email protected]> wrote:
> Eduardo Nunez <[email protected]>:
> 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 <[email protected]> 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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