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From |
Rebecca Pope <rebecca.a.pope@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: "pooled" xtmepoisson with unconstrained error variance |

Date |
Mon, 26 Nov 2012 08:36:48 -0600 |

Thanks Steve. I'll try it out and let you know. Best, Rebecca On Sun, Nov 25, 2012 at 5:35 PM, Steve Samuels <sjsamuels@gmail.com> wrote: > > > Bobby Gutierrez showed how to model unequal variances with -xtmixed- in > www.stata.com/meeting/fnasug08/gutierrez.pdf. I don't have time to try > this, but at first glance it looks like his trick for doing this (pp > 14-16) will work with -xtmelogit-. > > Steve > > On Nov 25, 2012, at 4:59 PM, Rebecca Pope wrote: > > Hello, > I need to estimate a Poisson model for two groups with unequal > variance where the data is comes from observations on patients over > time nested within clinics (i.e. level 1 is time (measurement > occasion), level 2 is the patient, and level 3 is the clinic). I am > using Stata 12.1. I think -xtmepoisson- is a natural choice for the > analysis, except that for the time being I'm stuck estimating separate > equations for each group. > > In the interest of fixing terms, by "pooled" I mean that I've taken a > separate equation for each group and written them as one "master" > equation. > > Bill Gould discusses something similar to my problem in the linear > regression context at: > http://www.stata.com/support/faqs/statistics/pooling-data-and-chow-tests/. > aweights and -xtglm- are discussed, but neither is applicable in this > context so I'm turning to the Statalist for assistance. > > Put as concisely as I can my questions are: > 1. Is unequal variance between groups as much of a problem in Poisson > models as in linear regression? (Clearly I think "yes" or I wouldn't > be posting, but I'd like to verify with more expert folks than me). > 2a. Can I control for this in a "pooled" multilevel Poisson model (in Stata)? > 2b. How do I control for unequal variance in a pooled multilevel > Poisson model in Stata? > > Here is an example that resembles my problem. Assume for the sake of > argument that a group*age interaction is somehow meaningful and > interesting in this context. > > *** begin example *** > use http://www.stata-press.com/data/r12/epilepsy > /* create artificial groups, 1 for odd ID number, 0 for even */ > gen foo = ceil((subject/2)-int(subject/2)) > /* demonstrate baseline differences in variances by group */ > by subject, sort: gen first=_n==1 > sdtest seizures if first, by(foo) /* significant at alpha=0.10, in > actual data, p < 0.001 */ > /* -xtmepoisson- model from manual for each group (1) */ > by foo, sort : xtmepoisson seizures treat lage lbas lbas_trt v4, || subject: > /* -xtmepoisson- with interactions for covariate of interest (2) */ > xtmepoisson seizures treat lage##i.foo lbas lbas_trt v4, || subject: > /* -xtmepoisson- fully interacted (3) (will switch to Laplace here > by default) */ > gen cons0=foo==0 > xtmepoisson seizures cons0 i.foo##i.treat c.lage##i.foo c.lbas##i.foo > c.lbas_trt##i.foo c.v4##i.foo, nocons || subject: R.foo > > *** end example *** > > (3) seems to me to be clearly preferred to (2) because it recovers all > FEs from (1) though the estimates are not exact. I tried Laplace in > both and it didn't make a difference, which from the manual should > have been expected. Am I on the right track with this progression? How > do I accommodate the fact that the variance in number of seizures > differs by "foo"? > > In case the following is relevant to anyone's recommendations: > - The example above only has 59 patients; I have several of thousand. > - I do not have an equal number of patients in each group; there is > about a 3:1 ratio of 0s to 1s for my comorbidity indicator. > - The data is observational. It comes from medical records review. > - There are about 30 coefficients to be estimated before any interactions/REs. > - There is no randomly assigned treatment, just a set of 3 covariates > that I am interested in testing whether they are jointly different > between the two groups. > - The example data doesn't have a natural level 3 variable, but I have > a random intercept for the clinic also. > > Related econometric references are welcomed just as much as Stata tips > because I'd really like to learn more about this. I've tried searching > with the terms "pooling Poisson multilevel mixed effects" and various > combinations thereof and haven't found anything that addresses the use > of pooled data in a Poisson regression let alone the issue of unequal > variances. > > * I'm not sure if the use of R.foo is correct for the RE in model (3). > It is my best guess for now & I intend to do more reading on that > later. > > Thanks, > Rebecca > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/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/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: "pooled" xtmepoisson with unconstrained error variance***From:*Rebecca Pope <rebecca.a.pope@gmail.com>

**Re: st: "pooled" xtmepoisson with unconstrained error variance***From:*Steve Samuels <sjsamuels@gmail.com>

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