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Re: st: Binomial regression


From   Garry Anderson <g.anderson@unimelb.edu.au>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Binomial regression
Date   Tue, 07 Aug 2007 19:09:58 +1000

It is interesting that when the poisson regression is used with the auto data (see below under Roger Harbord wrote:) and the
-predict predn, n-
command is used to predict the number of events, the predicted number of events for line 71 of the data is 1.789. Although the poisson regression can make predictions greater than one, does this imply that when using poisson regression to estimate relative risks that it should not be used for estimating the probability of the event?

If so, how would the probability of an event be estimated?

Kind regards, Garry

At 09:01 PM 6/08/2007 +0100, you wrote:


Richard Goldstein wrote:
> I have just returned from Salt Lake City to find this
> interesting discussion.  Although I have nothing to add
> for the situation where the identity link is used, I
> note that others have complained about failure to
> converge when using the log link (relative risk
> interpretation).  For those cases, there is a solution:
> use poisson regression with robust error variance;
> see Zou, G (2004), "A Modified Poisson regression
> approach to prospective studies with binary data,"
> _American Journal of Epidemiology_, 159: 702-706.
>
> Rich
Roger Harbord wrote:

  poisson for price mpg, robust nolog

Poisson regression                                Number of obs
=         74
                                                   Wald chi2(2)    =
27.23
                                                   Prob > chi2     =
0.0000
Log pseudolikelihood = -43.959394                 Pseudo R2       =
0.0971

------------------------------------------------------------------------------
              |               Robust
      foreign |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------------
        price |   .0001289   .0000593     2.17   0.030     .0000127
0002451
          mpg |   .1052061   .0201699     5.22   0.000     .0656738
1447385
        _cons |  -4.427599   .7772089    -5.70   0.000    -5.950901
-2.904298
------------------------------------------------------------------------------



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