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Re: st: Multi-level discrete time survival analysis


From   Robert A Yaffee <bob.yaffee@nyu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Multi-level discrete time survival analysis
Date   Sun, 27 Nov 2011 04:56:12 -0500

Dear HH,
   You can use xtmelogit or gllamm for such a design.   You can model the
repeated occasions at level one,  the children at level two, and the
treatment facilities at level three. For details, refer to Multi-level and
longitudinal modeling using Stata by Sophia Rabe-Hasketh and Anders Skrondal,
2nd ed, Chapter 10.
     - Regards,
          Bob

On Sat, Nov 26, 2011 at 3:26 PM, 0 1 <hhholmes61@gmail.com> wrote:
> I am trying to compare 50 treatment facilities on the likelihood of
> their children “graduating” from the program within 3 years (36
> months). I would also like to control for the “speed” at which their
> children graduate, using length of stay (LOS) in discrete time
> intervals of 0-5 mo, 6-11 mo, 12-23 mo, and 24-36 mo. (Children still
> in treatment after 36 months would be censored.) So, basically: Which
> programs discharge the most children within 3 years, and do it the
> fastest? My thought is that I could use each facility's coefficient
> (or some other output) to "rank" them.
>
> Is a (multi-level) discrete time survival analysis the best approach
> to address this question? If so, would a multi-level discrete model in
> STATA yield a single coefficient for each facility that would reflect
> that facility's "performance" related to the likelihood and speed of
> graduating children by 36 months (controlling for age, etc.)?
>
> Some more details about the data:
>
> 1. I have 3 years of data on about 50,000 children (about 330 per
> facility, per year). Although I have LOS in days (from date of entry
> to date of graduation), with these data I understand it’s better to
> treat time in discrete intervals like the ones I listed.  This is
> because ties are common: a lot of kids tend to leave at the end of the
> month, or end of the year, etc., due in part to insurance rules. Plus,
> many treatment strategies are built around these intervals, so they
> have an important meaning.
>
> 2. My data are arranged so each child has one record for each discrete
> time period that he was observed (i.e., person-period format).
>
> 3. My event variable is GRADUATE (1 = Yes, 0 = No). The child is
> censored if his LOS exceeds 36 months, or if he is still in treatment
> when data collection stops.
>
> 4. My time variables are four dummy variables (d1, d2, d3, d4) that
> represent the LOS intervals (0-5 mo, 6-11, mo, etc.) with a “1” if the
> child was observed in that interval and a 0 for the remaining.
>
> 5. I  also have some covariates that I would like to control for:
>
> STARTYEAR – Year child entered the program (each year is a cohort of children)
> AGE – Age of child when he entered
>
> ###
>
> Thank you for any insights. I'm new to STATA and new to survival
> analysis, so bear with me.
>
> HH
>
> *
> *   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/
>



-- 
Robert A. Yaffee, Ph.D.
Research Professor
Silver School of Social Work
New York University

Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf

CV:  http://homepages.nyu.edu/~ray1/vita.pdf

*
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*   http://www.stata.com/help.cgi?search
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