I am trying to fit a GEE Poisson model on a panel dataset consisting of T=360 and N=304 for a total of >108,000 observations in Stata. My response variable measures a count of people imprisoned, and I am interested in the effect of three dummy variables compared to a baseline scenario (which are originally a factor variable called "intervention2". I am new to this class of models so pardon me if the questions sounds dumb. To exemplify, the syntax used is the following:
Code:
xtgee o1 o5a o6a i.intervention2 i.decade, family(poisson) link(log) corr(exchangeable)
I attach below some output examples
# EXAMPLE 1: EXCHANGEABLE STRUCTURE
Code:
. xtgee o1 o5a o6a i.intervention2 i.decade, family(poisson) link(log) corr(exchangeable) Iteration 1: tolerance = .84106964 Iteration 2: tolerance = .48496633 Iteration 3: tolerance = .20312782 Iteration 4: tolerance = .10553827 Iteration 5: tolerance = .03057151 Iteration 6: tolerance = .00465901 Iteration 7: tolerance = .00267481 Iteration 8: tolerance = .00115663 Iteration 9: tolerance = .00024735 Iteration 10: tolerance = .00007535 Iteration 11: tolerance = .00005332 Iteration 12: tolerance = .00001348 Iteration 13: tolerance = 3.161e-06 Iteration 14: tolerance = 2.087e-06 Iteration 15: tolerance = 7.841e-07 GEE population-averaged model Number of obs = 108,000 Group variable: run_id Number of groups = 300 Link: log Obs per group: Family: Poisson min = 360 Correlation: exchangeable avg = 360.0 max = 360 Wald chi2(8) = 17508.34 Scale parameter: 1 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------- o1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- o5a | 2.334807 .0620962 37.60 0.000 2.2131 2.456513 o6a | 1.631073 .014881 109.61 0.000 1.601907 1.660239 | intervention2 | disruptive | -1.785197 .0360908 -49.46 0.000 -1.855933 -1.71446 facilitators | .0015218 .021609 0.07 0.944 -.040831 .0438746 preventive | -.5608344 .0251605 -22.29 0.000 -.610148 -.5115207 students | .4755749 .0203458 23.37 0.000 .4356979 .5154519 | decade | 2 | -.0094256 .0009321 -10.11 0.000 -.0112525 -.0075987 3 | -.1130466 .001251 -90.36 0.000 -.1154986 -.1105946 | _cons | 2.324379 .0234435 99.15 0.000 2.27843 2.370327 -------------------------------------------------------------------------------
# EXAMPLE 2: INDEPENDENT STRUCTURE
Code:
xtgee o1 o5a o6a i.intervention2 i.decade, family(poisson) link(log) corr(independent) Iteration 1: tolerance = 1.627e-11 GEE population-averaged model Number of obs = 108,000 Group variable: run_id Number of groups = 300 Link: log Obs per group: Family: Poisson min = 360 Correlation: independent avg = 360.0 max = 360 Wald chi2(8) = 4733.11 Scale parameter: 1 Prob > chi2 = 0.0000 Pearson chi2(108000): 183882.66 Deviance = 173787.77 Dispersion (Pearson): 1.702617 Dispersion = 1.609146 ------------------------------------------------------------------------------- o1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- o5a | 4.589151 .1899733 24.16 0.000 4.21681 4.961491 o6a | .5389832 .018326 29.41 0.000 .5030649 .5749016 | intervention2 | disruptive | -.0873664 .003151 -27.73 0.000 -.0935423 -.0811906 facilitators | -.0306047 .0031123 -9.83 0.000 -.0367047 -.0245047 preventive | -.0515957 .0031221 -16.53 0.000 -.0577149 -.0454766 students | -.0169098 .0031137 -5.43 0.000 -.0230125 -.0108072 | decade | 2 | -.0044688 .0030841 -1.45 0.147 -.0105135 .001576 3 | -.1194673 .0030201 -39.56 0.000 -.1253864 -.1135481 | _cons | 1.747254 .0201088 86.89 0.000 1.707842 1.786667 -------------------------------------------------------------------------------
Code:
xtgee o1 o5a o6a i.intervention2 i.decade, family(poisson) link(log) corr(ar1) Iteration 1: tolerance = .59377955 Iteration 2: tolerance = .02410303 Iteration 3: tolerance = .00982333 Iteration 4: tolerance = .00135473 Iteration 5: tolerance = .00015177 Iteration 6: tolerance = .00001665 Iteration 7: tolerance = 1.781e-06 Iteration 8: tolerance = 1.900e-07 GEE population-averaged model Number of obs = 108,300 Group and time vars: run_id step Number of groups = 300 Link: log Obs per group: Family: Poisson min = 361 Correlation: AR(1) avg = 361.0 max = 361 Wald chi2(8) = 420.30 Scale parameter: 1 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------- o1 | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- o5a | 1.318071 .1008281 13.07 0.000 1.120452 1.51569 o6a | .2987947 .0203005 14.72 0.000 .2590064 .338583 | intervention2 | disruptive | -.1787983 .046164 -3.87 0.000 -.2692781 -.0883185 facilitators | -.0823756 .0451025 -1.83 0.068 -.1707749 .0060236 preventive | -.0417277 .0445182 -0.94 0.349 -.1289818 .0455264 students | -.0016382 .0441991 -0.04 0.970 -.0882669 .0849905 | decade | 2 | -.0014564 .0017892 -0.81 0.416 -.0049632 .0020504 3 | -.0035202 .0025268 -1.39 0.164 -.0084726 .0014323 | _cons | 2.077726 .0336093 61.82 0.000 2.011853 2.143599 -------------------------------------------------------------------------------
0 Response to GEE: results significant only with independent and exchangeable correlation structure
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