Hi Ken, I ran both sas 9.3 and sas 9.4 today, but the coefficients are still different from the stata command given. The codes and results are as follows Stata codes: xtset patient visit #delimit ; xtpoisson y x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16, exposure(visit) normal; #delimit cr Stata results Fitting comparison Poisson model: Iteration 0:   log likelihood =  -13626.58  Iteration 1:   log likelihood = -13626.554  Iteration 2:   log likelihood = -13626.554  Fitting full model: tau =  0.0     log likelihood = -13626.554 tau =  0.1     log likelihood = -13563.763 tau =  0.2     log likelihood =  -13549.87 tau =  0.3     log likelihood =  -13562.89 Iteration 0:   log likelihood = -13549.935  Iteration 1:   log likelihood = -13547.007  Iteration 2:   log likelihood = -13546.996  Iteration 3:   log likelihood = -13546.996  Random-effects Poisson regression               Number of obs      > =                                                                >      100000 Group variable: patient                         Number of groups   > =                                                                >        2391 Random effects u_i ~ Gaussian                   Obs per group: min > =                                                                >           1                                                                avg > =                                                                >        41.8                                                                max > =                                                                >         231 Integration method: mvaghermite                 Integration points > =                                                                >          12                                                 Wald chi2(16)      > =                                                                >      308.08 Log likelihood  = -13546.996                    Prob > chi2        > =                                                                >      0.0000 ------------------------------------------------------------------- > -----------       y |      Coef.   Std. Err.      z    P>|z|     [95% Conf > . Interval] -------------+----------------------------------------------------- > -----------         x1 |  -.0023401   .0019663    -1.19   0.234    -.0061939 >    .0015137         x2 |   .0791925   .0509703     1.55   0.120    -.0207075 >    .1790925         x3 |  -.0556415   .0857818    -0.65   0.517    -.2237707 >    .1124877         x4 |   .2604183   .1530857     1.70   0.089    -.0396241 >    .5604608         x5|   .0260236   .1988315     0.13   0.896     -.363679 >    .4157261         x6 |   .0096076   .0558143     0.17   0.863    -.0997864 >    .1190016         x7 |   .8417932   .0595242    14.14   0.000     .7251279 >    .9584586         x8 |  -.0860519   .0591174    -1.46   0.146    -.2019199 >     .029816        x9 |  -.3834076   .0742257    -5.17   0.000    -.5288874 >   -.2379278         x10|   .0169214   .0610793     0.28   0.782    -.1027917 >    .1366346        x11 |  -.2420708   .0549503    -4.41   0.000    -.3497714 >   -.1343701        x12 |  -.3106347   .0593382    -5.23   0.000    -.4269354 >    -.194334        x13 |  -.1306361   .0632131    -2.07   0.039    -.2545315 >   -.0067407        x14|  -.1735819   .0566482    -3.06   0.002    -.2846104 >   -.0625535       x15 |  -.2393673   .0561851    -4.26   0.000    -.3494881 >   -.1292465       x16 |  -.0680811   .0809386    -0.84   0.400    -.2267179 >    .0905557        _cons |  -7.036347   .1595935   -44.09   0.000    -7.349144 >   -6.723549    ln(visit) |          1  (exposure) -------------+----------------------------------------------------- > -----------     /lnsig2u |  -1.364564   .1253317   -10.89   0.000    -1.610209 >   -1.118918 -------------+----------------------------------------------------- > -----------      sigma_u |   .5054623   .0316752                      .4470412 >    .5715182 ------------------------------------------------------------------- > ----------- Likelihood-ratio test of sigma_u=0: chibar2(01) =   159.11 Pr>=chib > ar2 = 0.000 SAS 9.4 codes proc countreg data=out_sample1 dist=poisson groupid=patient ; model y = x1 x2 x3 x4 x5x 6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 / errorcomp=random offset=logvisit; run; SAS 9.4 results The COUNTREG Procedure Model Fit Summary Dependent Variable    y Number of Observations    100000 Data Set    WORK.OUT_SAMPLE1 Model    Poisson Error Component    Random Number of Cross Sections    2391 Offset Variable    logvisit Log Likelihood    -13554 Maximum Absolute Gradient    9.56066E-7 Number of Iterations    15 Optimization Method    Newton-Raphson AIC    27143 SBC    27315 Algorithm converged. Parameter Estimates Parameter    DF    Estimate    Standard Error    t Value    Pr > |t| Intercept    1    -6.899390    0.156789    -44.00    <.0001 x1    1    -0.002426    0.001927    -1.26    0.2079 x2    1    0.091417    0.050119    1.82    0.0682 x3    1    -0.067597    0.084548    -0.80    0.4240 x4    1    0.267802    0.154460    1.73    0.0830 x5    1    0.039801    0.197891    0.20    0.8406 x6    1    0.003591    0.054764    0.07    0.9477 x7    1    0.848595    0.058601    14.48    <.0001 x8    1    -0.085057    0.058143    -1.46    0.1435 x9    1    -0.384157    0.072340    -5.31    <.0001 x10    1    0.022466    0.060081    0.37    0.7085 x11    1    -0.253048    0.054064    -4.68    <.0001 x12    1    -0.320980    0.058543    -5.48    <.0001 x13    1    -0.146113    0.062766    -2.33    0.0199 x14    1    -0.172344    0.055604    -3.10    0.0019 x15    -0.241250    0.055214    -4.37    <.0001 x16    1    -0.074001    0.079395    -0.93    0.3513 _Alpha    1    0.229979    0.028888    7.96    <.0001 SAS 9.3 codes proc tcountreg data=out_sample1 dist=poisson; id patient; model y= x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 / errorcomp=random offset=logvisit; run; SAS 9.3 results The TCOUNTREG Procedure Model Fit Summary Dependent Variable    y Number of Observations    100000 Data Set    WORK.OUT_SAMPLE1 Model    Poisson Error Component    Random Offset Variable    logvisit Log Likelihood    -13557 Maximum Absolute Gradient    1.12475E-6 Number of Iterations    14 Optimization Method    Newton-Raphson AIC    27151 SBC    27322 Algorithm converged. Parameter Estimates Parameter    DF    Estimate    Standard Error    t Value    Pr > |t| Intercept    1    -6.899832    0.153968    -44.81    <.0001 x1    1    -0.002457    0.001891    -1.30    0.1938 x2    1    0.086361    0.049271    1.75    0.0796 x3    1    -0.070813    0.083061    -0.85    0.3939 x4    1    0.263515    0.152089    1.73    0.0832 x5    1    0.030784    0.195286    0.16    0.8747 x6    1    -0.000162    0.053809    -0.00    0.9976 x7    1    0.837899    0.057722    14.52    <.0001 x8    1    -0.084471    0.057015    -1.48    0.1385 x9    1    -0.373889    0.071050    -5.26    <.0001 x10    1    0.019545    0.058936    0.33    0.7402 x11    1    -0.246067    0.053034    -4.64    <.0001 x12    1    -0.315442    0.057560    -5.48    <.0001 x13    1    -0.141599    0.061752    -2.29    0.0218 x14    1    -0.175507    0.054645    -3.21    0.0013 x15    1    -0.241094    0.054212    -4.45    <.0001 x16    1    -0.080489    0.077757    -1.04    0.3006 _Alpha    1    0.222232    0.028434    7.82    <.0001 Is this difference due to "unbalanced"panel data? I noticed stata results indicate panel variable:  patient (unbalanced). Thanks!! alphabeta ... View more

Hi Ken, Somehow running sas 9.3 gives me error message as follows: 271  proc countreg data=two dist=poisson groupid=ship ;                                          -------                                          22                                          76 ERROR 22-322: Syntax error, expecting one of the following: ;, ABSFCONV, ABSGCONV, CORRB, COVB,               COVEST, COVOUT, DATA, DIST, FCONV, GCONV, ITPRINT, MAXITER, METHOD, NOPRINT,               OUTEST, PRINTALL, SEED, TYPE. ERROR 76-322: Syntax error, statement will be ignored. If change the codes as proc countreg data=two dist=poisson; by ship ; then sas gives me another error message 284  proc countreg data=two dist=poisson; 285  by ship ; 286  model accident=op co_65_69 co_70_74 co_75_79/ errorcomp=random offset=lnservice;                                                    ---------                                                    22                                                    76 ERROR 22-322: Syntax error, expecting one of the following: ;, CORRB, COVB, COVEST, COVOUT,               DIST, ITPRINT, METHOD, NOINT, NOPRINT, OFFSET, OUTEST, PRINTALL, TYPE. ERROR 76-322: Syntax error, statement will be ignored. Can you take a look for me? Thanks!! ... View more

Hi all, For the predict part, I figured out lnlm=log(pred/visit), pred=exp(lnlm)*visit I didn't divide back the exposure variable, that is why stata and sas were different. For the coefficients, somehow fitting simple poisson regression was not a problem, but fitting a random effect poisson regression using stata and sas gives me very different coefficient estimates. Still not having a clue yet. alphabeta ... View more

Hi Ken, Thanks for your method. I tried this method with both fixed and random effect, but the coefficients are still not close either. Thanks! alphabeta ... View more

Hi Jacob, I tried different covariance structure options, but the coefficients are still different. I was only given the stata codes, but I assume the original coder wants to fit a random effect poisson regression with an exposure variable, and the random effect follows a normal distribution based on the code. Thanks! alphabeta ... View more