Re: st: logistic regression complex samples

[Antonio silva <asilva@inbox.com>]

Thanks for the replies. I can run a model using  SAS surveylogistic without  the cluster variable but I have had  difficulties to do the same with Stata version 11. I am a beginner in Stata programming.My final goal is to calculate the Archer  and  Lemeshow  (A-L;  2006) goodness of fit test (with estat gof command) that is not available in SAS. To do that I have to  run correctly the logistic regression model (with only weight and strata without cluster) in Stata. I hope someone can help with the Stata code.
Consider the following  code (ex. with 2 categorical covariates)  that have been used and the output .

svyset [pweight= var_weight], strata(var_strata)


.  xi: svy: logistic outcome i.covar1  i.covar2_3cat 


i.covar1            _Icovar1_1-2          (naturally coded; _Icovar1_1 omitted)
i.covar2_3cat    _Icovar2_3_1-3     (naturally coded; _Icovar2_3_1 omitted)
(running logistic on estimation sample)

Survey: Logistic regression

Number of strata   =         9                  Number of obs      =       398
Number of PSUs     =       398                  Population size    = 4361.1088
                                                Design df          =       389
                                                F(   0,    389)    =         .
                                                Prob > F           =         .

------------------------------------------------------------------------------
             |             Linearized
      outcome | Odds Ratio   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    _Icovar1_2 |   1.926984          .        .       .            .           .
_Icovar2_~2 |   .2875105          .        .       .            .           .
_Icovar2_~3 |   .1978389          .        .       .            .           .
------------------------------------------------------------------------------
Note: missing standard errors because of stratum with single sampling unit.

Thanks,
Antonio.
> -----Original Message-----
> From: skolenik@gmail.com
> Sent: Wed, 7 Dec 2011 11:18:37 -0600
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: logistic regression complex samples
> 
> Antonio,
> 
> it would help if you mentioned the version of Stata that you are
> using. By default, Stata would use observations as PSUs (and the
> output of -svyset- would state that -- again, it would help if you
> included the output of both commands). You can also achieve the effect
> of specifying observations as PSUs via -svyset _n ...-.
> 
> On Wed, Dec 7, 2011 at 10:05 AM, Antonio silva <asilva@inbox.com> wrote:
>> Hello,
>> I would like to perform binary logistic regression in stratified
>> sampling incorporating 2 variables that represents that design
>> var_weight and var_strata.
>> Considering a model with 2 covariates , in SAS I would consider a code
>> like this that works perfectly:
>> 
>> PROC SURVEYLOGISTIC DATA =  dataset
>> STRATA var_strata;
>> 
>> WEIGHT var_weight;
>> 
>> 
>> CLASS covariate1
>>      Covariate2  ;
>> 
>> MODEL outcome(event='1')= covariate1 covariate2 /clparm vadjust=none ;
>>  Run;
>> 
>> 
>> I tried an equivalent Stata code but does not work. It seems that in
>> Stata its is always necessary have the cluster variable. But in my
>> design I do not have cluster variable,only weight and strata.
>> 
>> svyset [pweight= var_weight], strata(var_strata)
>> 
>>  svy: logistic outcome i.covariate1 i.covariate2
>> 
>> After run , in the output appears only the OR calculated and a note:
>> Note: missing standard errors because of stratum with single sampling
>> unit.
>> What is wrong with it?
>> 
>> After that I did some tests considering a fictitious cluster variable
>> and worked.   I suppose this command works only when the 3 design
>> variables weight strata and cluster are used at the same time.
> 
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
> 
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