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st: xi command


From   Asa Odenbro <Asa.Odenbro@mep.ki.se>
To   statalist@hsphsun2.harvard.edu
Subject   st: xi command
Date   Wed, 13 Aug 2003 14:35:38 +0200

Hi Stata users,

I am a new user of Stata and I am doing a project on squamous cell cancer and smoking habits in the construction workers cohort in Sweden.

I have been trying to do Poisson regression with the xi command, but it has been giving me some trouble. 2 questions have arisen:


1.	What is wrong with the xi3 command? I saw on stata list that someone had noticed in february that there is something wrong with it but I never found a good answer. He had the same problem as I have had. Command:
xi: poisson outcome i.variable1*i.varable2*i.variable3, exposure(….
Stata output: varlist not allowed

2.	When I use xi with multiple interaction terms, stata tells me he is dropping a bunch of variables due to collinearity, but then it looks like he has not dropped them at all (please see stata output below). But if I then put xi3 in front of the command stata does not say he is dropping any variables and the results are exactly the same!!


. xi: poisson scc i.smokcatn*i.ageband  i.smokcatn*i.tertbmi i.tertbmi*i.ageban
> d, e(time_ageband) irr
i.smokcatn        _Ismokcatn_1-3      (naturally coded; _Ismokcatn_1 omitted)
i.ageband         _Iageband_10-60     (naturally coded; _Iageband_60 omitted)
i.smo~n*i.age~d   _IsmoXage_#_#       (coded as above)
i.tertbmi         _Itertbmi_0-2       (naturally coded; _Itertbmi_0 omitted)
i.smo~n*i.ter~i   _IsmoXter_#_#       (coded as above)
i.ter~i*i.age~d   _IterXage_#_#       (coded as above)
note: _Ismokcatn_2 dropped due to collinearity
note: _Ismokcatn_3 dropped due to collinearity
note: _Itertbmi_1 dropped due to collinearity
note: _Itertbmi_2 dropped due to collinearity
note: _Iageband_10 dropped due to collinearity
note: _Iageband_50 dropped due to collinearity

Iteration 0:   log likelihood = -3081.9574
Iteration 1:   log likelihood =  -2975.207
Iteration 2:   log likelihood = -2939.4822
Iteration 3:   log likelihood = -2936.4882
Iteration 4:   log likelihood = -2936.4841
Iteration 5:   log likelihood = -2936.4841

Poisson regression                                Number of obs   =     450957
LR chi2(18)     =     956.28
Prob > chi2     =     0.0000
Log likelihood = -2936.4841                       Pseudo R2       =     0.1400


scc         IRR   Std. Err.      z    P>z     [95% Conf. Interval]

_Ismokcatn_2    .6198224   .2198933    -1.35   0.178     .3092346    1.242357
_Ismokcatn_3    .9058287   .2208259    -0.41   0.685     .5617427    1.460679
_Iageband_10    .0094563    .004177   -10.55   0.000     .0039786    .0224753
_Iageband_50    .1100253   .0389725    -6.23   0.000     .0549521    .2202927
_IsmoXa~2_10    5.435618   3.145965     2.93   0.003     1.748237    16.90043
_IsmoXa~2_50    1.192367   .4748936     0.44   0.659     .5462543    2.602704
_IsmoXa~3_10    2.043416   1.086089     1.34   0.179     .7210039    5.791301
_IsmoXa~3_50    1.871118   .5963836     1.97   0.049     1.001836    3.494666
_Itertbmi_1    .8919676   .2137564    -0.48   0.633     .5576505    1.426711
_Itertbmi_2    .7871886   .1790034    -1.05   0.293     .5041028    1.229245
_IsmoXte~2_1    1.420636   .5827945     0.86   0.392     .6357558    3.174501
_IsmoXte~2_2    1.275247   .4983443     0.62   0.534     .5928705     2.74302
_IsmoXte~3_1    .8762735   .2716256    -0.43   0.670     .4772938    1.608768
_IsmoXte~3_2    .8243317    .244574    -0.65   0.515     .4608461    1.474511
_IterXa~1_10     .535335   .2989043    -1.12   0.263      .179208    1.599168
_IterXa~1_50    .7190481   .2815366    -0.84   0.400     .3337935    1.548952
_IterXa~2_10    .8560323   .4791763    -0.28   0.781     .2857708    2.564262
_IterXa~2_50    1.616486   .5536023     1.40   0.161     .8261498    3.162898
time_ageband  (exposure)


. xi3: poisson scc i.smokcatn*i.ageband  i.smokcatn*i.tertbmi i.tertbmi*i.ageba
> nd, e(time_ageband) irr
i.smokcatn        _Ismokcatn_1-3      (naturally coded; _Ismokcatn_1 omitted)
i.ageband         _Iageband_10-60     (naturally coded; _Iageband_60 omitted)
i.tertbmi         _Itertbmi_0-2       (naturally coded; _Itertbmi_0 omitted)

Iteration 0:   log likelihood = -3081.9574
Iteration 1:   log likelihood =  -2975.207
Iteration 2:   log likelihood = -2939.4822
Iteration 3:   log likelihood = -2936.4882
Iteration 4:   log likelihood = -2936.4841
Iteration 5:   log likelihood = -2936.4841

Poisson regression                                Number of obs   =     450957
LR chi2(18)     =     956.28
Prob > chi2     =     0.0000
Log likelihood = -2936.4841                       Pseudo R2       =     0.1400


scc         IRR   Std. Err.      z    P>z     [95% Conf. Interval]

_Ismokcatn_2    .6198224   .2198933    -1.35   0.178     .3092346    1.242357
_Ismokcatn_3    .9058287   .2208259    -0.41   0.685     .5617427    1.460679
_Iageband_10    .0094563    .004177   -10.55   0.000     .0039786    .0224753
_Iageband_50    .1100253   .0389725    -6.23   0.000     .0549521    .2202927
_Ism2Xag10      5.435618   3.145965     2.93   0.003     1.748237    16.90043
_Ism2Xag50      1.192367   .4748936     0.44   0.659     .5462543    2.602704
_Ism3Xag10      2.043416   1.086089     1.34   0.179     .7210039    5.791301
_Ism3Xag50      1.871118   .5963836     1.97   0.049     1.001836    3.494666
_Itertbmi_1     .8919676   .2137564    -0.48   0.633     .5576505    1.426711
_Itertbmi_2     .7871886   .1790034    -1.05   0.293     .5041028    1.229245
_Ism2Xte1       1.420636   .5827945     0.86   0.392     .6357558    3.174501
_Ism2Xte2       1.275247   .4983443     0.62   0.534     .5928705     2.74302
_Ism3Xte1       .8762735   .2716256    -0.43   0.670     .4772938    1.608768
_Ism3Xte2       .8243317    .244574    -0.65   0.515     .4608461    1.474511
_Ite1Xag10      .535335   .2989043    -1.12   0.263      .179208    1.599168
_Ite1Xag50      .7190481   .2815366    -0.84   0.400     .3337935    1.548952
_Ite2Xag10      .8560323   .4791763    -0.28   0.781     .2857708    2.564262
_Ite2Xag50      1.616486   .5536023     1.40   0.161     .8261498    3.162898
time_ageband  (exposure)


Grateful for help!

Regards Åsa Odenbro

Department for Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Asa.odenbro@mep.ki.se





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