------------------------------------------------------------------------------------------------- log: C:\MyDocs\MPH\Text\SecondEdition\WebDoFiles\9.3.Framingham.log log type: text opened on: 3 Jan 2008, 17:40:01 . * 9.3.Framingham.log . * . * Estimate the effect of age and gender on coronary heart disease (CHD) . * using several Poisson regression models . * . use C:\WDDtext\8.12.Framingham.dta, clear . * . * Fit a multiplicative model of the effect of gender and age on CHD . * . xi: poisson chd_cnt i.age_gr male, exposure(pt_yrs) irr i.age_gr _Iage_gr_45-81 (naturally coded; _Iage_gr_45 omitted) Iteration 0: log likelihood = -1559.7879 Iteration 1: log likelihood = -1559.2076 Iteration 2: log likelihood = -1559.2065 Iteration 3: log likelihood = -1559.2065 Poisson regression Number of obs = 1267 LR chi2(9) = 698.64 Prob > chi2 = 0.0000 Log likelihood = -1559.2065 Pseudo R2 = 0.1830 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Iage_gr_50 | 1.864355 .3337745 3.48 0.001 1.312618 2.648005 _Iage_gr_55 | 3.158729 .5058088 7.18 0.000 2.307858 4.323303 _Iage_gr_60 | 4.885053 .7421312 10.44 0.000 3.627069 6.579347 _Iage_gr_65 | 6.44168 .9620181 12.47 0.000 4.807047 8.632168 _Iage_gr_70 | 6.725369 1.028591 12.46 0.000 4.983469 9.076127 _Iage_gr_75 | 8.612712 1.354852 13.69 0.000 6.327596 11.72306 _Iage_gr_80 | 10.37219 1.749287 13.87 0.000 7.452702 14.43534 _Iage_gr_81 | 13.67189 2.515296 14.22 0.000 9.532967 19.60781 male | 1.996012 .1051841 13.12 0.000 1.800144 2.213192 pt_yrs | (exposure) ------------------------------------------------------------------------------ . estimates store age_male . * . * Tabulate patient-years of follow-up and number of . * CHD events by sex and age group. . * . table sex, contents(sum pt_yrs sum chd_cnt) by(age_gr) -------------------------------------- age_gr | and Sex | sum(pt_yrs) sum(chd_cnt) ----------+--------------------------- <= 45 | Men | 7370 43 Women | 9205 9 ----------+--------------------------- 45-50 | Men | 5835 53 Women | 7595 25 ----------+--------------------------- 50-55 | Men | 6814 110 Women | 9113 46 ----------+--------------------------- 55-60 | Men | 7184 155 Women | 10139 105 ----------+--------------------------- 60-65 | Men | 6678 178 Women | 9946 148 ----------+--------------------------- 65-70 | Men | 4557 121 Women | 7385 120 ----------+--------------------------- 70-75 | Men | 2575 94 Women | 4579 88 ----------+--------------------------- 75-80 | Men | 1205 50 Women | 2428 59 ----------+--------------------------- > 80 | Men | 470 19 Women | 1383 50 -------------------------------------- . * . * Calculate age-sex specific incidence of CHD . * . collapse (sum) patients = pt_yrs chd = chd_cnt, by(age_gr male) . generate rate = 1000*chd/patients . generate men = rate if male == 1 (9 missing values generated) . generate women = rate if male == 0 (9 missing values generated) . graph bar men women, over(age_gr) ytitle(CHD Morbidity Rate per 1000) /// > ylabel(0(5)40) ymtick(0 (1) 41) subtitle(Age, position(6)) /// > bar(1, color(red)) bar(2, color(blue)) /// > legend(order(1 "Men" 2 "Women") ring(0) position(11) col(1)) . more . use 8.12.Framingham.dta, clear . * . * Add interaction terms to the model . * . xi: poisson chd_cnt i.age_gr*male, exposure(pt_yrs) i.age_gr _Iage_gr_45-81 (naturally coded; _Iage_gr_45 omitted) i.age_gr*male _IageXmale_# (coded as above) Iteration 0: log likelihood = -1548.4211 Iteration 1: log likelihood = -1544.3776 Iteration 2: log likelihood = -1544.3226 Iteration 3: log likelihood = -1544.3226 Poisson regression Number of obs = 1267 LR chi2(17) = 728.41 Prob > chi2 = 0.0000 Log likelihood = -1544.3226 Pseudo R2 = 0.1908 ------------------------------------------------------------------------------ chd_cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Iage_gr_50 | 1.213908 .3887301 3.12 0.002 .4520111 1.975805 _Iage_gr_55 | 1.641461 .3644862 4.50 0.000 .9270816 2.355841 _Iage_gr_60 | 2.360093 .3473254 6.80 0.000 1.679348 3.040838 _Iage_gr_65 | 2.722564 .3433189 7.93 0.000 2.049671 3.395457 _Iage_gr_70 | 2.810563 .3456073 8.13 0.000 2.133185 3.487941 _Iage_gr_75 | 2.978378 .3499639 8.51 0.000 2.292462 3.664295 _Iage_gr_80 | 3.212992 .3578551 8.98 0.000 2.511609 3.914375 _Iage_gr_81 | 3.61029 .3620927 9.97 0.000 2.900601 4.319979 male | 1.786304 .3665609 4.87 0.000 1.067858 2.504751 _IageXmal~50 | -.7712728 .4395848 -1.75 0.079 -1.632843 .0902976 _IageXmal~55 | -.6237429 .4064443 -1.53 0.125 -1.420359 .1728733 _IageXmal~60 | -1.052307 .38774 -2.71 0.007 -1.812263 -.2923502 _IageXmal~65 | -1.203381 .3830687 -3.14 0.002 -1.954182 -.4525804 _IageXmal~70 | -1.295219 .3885418 -3.33 0.001 -2.056747 -.5336914 _IageXmal~75 | -1.144715 .395435 -2.89 0.004 -1.919754 -.369677 _IageXmal~80 | -1.251231 .4139034 -3.02 0.003 -2.062466 -.4399948 _IageXmal~81 | -1.674611 .4549709 -3.68 0.000 -2.566337 -.7828844 _cons | -6.930277 .3333333 -20.79 0.000 -7.583599 -6.276956 pt_yrs | (exposure) ------------------------------------------------------------------------------ . lincom male, irr ( 1) [chd_cnt]male = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 5.967359 2.187401 4.87 0.000 2.909142 12.24051 ------------------------------------------------------------------------------ . lincom male + _IageXmale_50, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_50 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 2.759451 .6695175 4.18 0.000 1.715134 4.439634 ------------------------------------------------------------------------------ . lincom male + _IageXmale_55, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_55 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 3.198115 .5615406 6.62 0.000 2.266919 4.511824 ------------------------------------------------------------------------------ . lincom male + _IageXmale_60, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_60 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 2.083393 .2633282 5.81 0.000 1.626239 2.669057 ------------------------------------------------------------------------------ . lincom male + _IageXmale_65, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_65 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 1.791267 .1992637 5.24 0.000 1.44036 2.227663 ------------------------------------------------------------------------------ . lincom male + _IageXmale_70, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_70 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 1.634089 .2105236 3.81 0.000 1.269443 2.103478 ------------------------------------------------------------------------------ . lincom male + _IageXmale_75, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_75 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 1.899497 .2817533 4.33 0.000 1.420298 2.540374 ------------------------------------------------------------------------------ . lincom male + _IageXmale_80, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_80 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 1.707574 .3282327 2.78 0.005 1.171549 2.488851 ------------------------------------------------------------------------------ . lincom male + _IageXmale_81, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_81 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 1.11817 .3013496 0.41 0.679 .6593363 1.896308 ------------------------------------------------------------------------------ . lrtest age_male . Likelihood-ratio test LR chi2(8) = 29.77 (Assumption: age_male nested in .) Prob > chi2 = 0.0002 . more . * . * Refit model with interaction terms using fewer parameters. . * . generate age_gr2 = recode(age_gr, 45,55,60,80,81) . xi: poisson chd_cnt i.age_gr2*male, exposure(pt_yrs) irr i.age_gr2 _Iage_gr2_45-81 (naturally coded; _Iage_gr2_45 omitted) i.age_gr2*male _IageXmale_# (coded as above) Iteration 0: log likelihood = -1567.49 Iteration 1: log likelihood = -1563.8441 Iteration 2: log likelihood = -1563.8267 Iteration 3: log likelihood = -1563.8267 Poisson regression Number of obs = 1267 LR chi2(9) = 689.40 Prob > chi2 = 0.0000 Log likelihood = -1563.8267 Pseudo R2 = 0.1806 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Iage_gr2_55 | 4.346254 1.537835 4.15 0.000 2.172374 8.695522 _Iage_gr2_60 | 10.59194 3.678848 6.80 0.000 5.362058 20.92277 _Iage_gr2_80 | 17.43992 5.876003 8.48 0.000 9.010533 33.75502 _Iage_gr2_81 | 36.97678 13.38902 9.97 0.000 18.18508 75.18702 male | 5.967359 2.187401 4.87 0.000 2.909142 12.24051 _IageXmal~55 | .5081774 .1998025 -1.72 0.085 .2351497 1.098212 _IageXmal~60 | .3491315 .1353723 -2.71 0.007 .1632842 .7465071 _IageXmal~80 | .2899566 .1081168 -3.32 0.001 .1396186 .6021749 _IageXmal~81 | .1873811 .0852529 -3.68 0.000 .0768164 .4570857 pt_yrs | (exposure) ------------------------------------------------------------------------------ . lincom male + _IageXmale_55, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_55 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 3.032477 .4312037 7.80 0.000 2.294884 4.007138 ------------------------------------------------------------------------------ . lincom male + _IageXmale_60, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_60 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 2.083393 .2633282 5.81 0.000 1.626239 2.669057 ------------------------------------------------------------------------------ . lincom male + _IageXmale_80, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_80 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 1.730275 .1182042 8.03 0.000 1.51344 1.978177 ------------------------------------------------------------------------------ . lincom male + _IageXmale_81, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_81 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 1.11817 .3013496 0.41 0.679 .6593363 1.896308 ------------------------------------------------------------------------------ . * . * Repeat previous analysis restricted to records with . * non-missing bmi values. . * . quietly xi: poisson chd_cnt i.age_gr2*male if !missing(bmi_gr) /// > , exposure(pt_yrs) . estimates store age_male_interact . * . * Adjust analysis for body mass index (BMI) . * . xi: poisson chd_cnt i.age_gr2*male i.bmi_gr, exposure(pt_yrs) i.age_gr2 _Iage_gr2_45-81 (naturally coded; _Iage_gr2_45 omitted) i.age_gr2*male _IageXmale_# (coded as above) i.bmi_gr _Ibmi_gr_1-4 (_Ibmi_gr_1 for bmi~r==22.79999923706055 omitted) Iteration 0: log likelihood = -1529.2637 Iteration 1: log likelihood = -1526.377 Iteration 2: log likelihood = -1526.3585 Iteration 3: log likelihood = -1526.3585 Poisson regression Number of obs = 1234 LR chi2(12) = 754.20 Prob > chi2 = 0.0000 Log likelihood = -1526.3585 Pseudo R2 = 0.1981 ------------------------------------------------------------------------------ chd_cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Iage_gr2_55 | 1.426595 .3538794 4.03 0.000 .7330039 2.120186 _Iage_gr2_60 | 2.293218 .3474423 6.60 0.000 1.612244 2.974193 _Iage_gr2_80 | 2.768015 .3371378 8.21 0.000 2.107237 3.428793 _Iage_gr2_81 | 3.473889 .3625129 9.58 0.000 2.763377 4.184401 male | 1.665895 .3669203 4.54 0.000 .9467446 2.385046 _IageXmal~55 | -.6387423 .3932103 -1.62 0.104 -1.40942 .1319357 _IageXmal~60 | -.9880223 .3878331 -2.55 0.011 -1.748161 -.2278834 _IageXmal~80 | -1.147882 .3730498 -3.08 0.002 -1.879046 -.4167178 _IageXmal~81 | -1.585361 .4584837 -3.46 0.001 -2.483972 -.6867493 _Ibmi_gr_2 | .231835 .08482 2.73 0.006 .0655909 .3980791 _Ibmi_gr_3 | .4071791 .0810946 5.02 0.000 .2482366 .5661216 _Ibmi_gr_4 | .6120817 .0803788 7.61 0.000 .4545421 .7696213 _cons | -7.165097 .3365738 -21.29 0.000 -7.824769 -6.505424 pt_yrs | (exposure) ------------------------------------------------------------------------------ . lrtest age_male_interact . Likelihood-ratio test LR chi2(3) = 66.90 (Assumption: age_male_int~t nested in .) Prob > chi2 = 0.0000 . quietly xi: poisson chd_cnt i.age_gr2*male i.bmi_gr /// > if !missing(scl_gr) , exposure(pt_yrs) . estimates store age_male_interact_bmi . * . * Adjust estimates for BMI and serum cholesterol . * . xi: poisson chd_cnt i.age_gr2*male i.bmi_gr i.scl_gr, exposure(pt_yrs) i.age_gr2 _Iage_gr2_45-81 (naturally coded; _Iage_gr2_45 omitted) i.age_gr2*male _IageXmale_# (coded as above) i.bmi_gr _Ibmi_gr_1-4 (_Ibmi_gr_1 for bmi~r==22.79999923706055 omitted) i.scl_gr _Iscl_gr_197-256 (naturally coded; _Iscl_gr_197 omitted) Iteration 0: log likelihood = -1462.0976 Iteration 1: log likelihood = -1460.2427 Iteration 2: log likelihood = -1460.2162 Iteration 3: log likelihood = -1460.2162 Poisson regression Number of obs = 1134 LR chi2(15) = 827.10 Prob > chi2 = 0.0000 Log likelihood = -1460.2162 Pseudo R2 = 0.2207 ------------------------------------------------------------------------------ chd_cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Iage_gr2_55 | 1.355069 .3539889 3.83 0.000 .6612635 2.048875 _Iage_gr2_60 | 2.177978 .3477139 6.26 0.000 1.496471 2.859485 _Iage_gr2_80 | 2.606269 .3376422 7.72 0.000 1.944502 3.268035 _Iage_gr2_81 | 3.254861 .3634038 8.96 0.000 2.542603 3.96712 male | 1.569233 .3671214 4.27 0.000 .8496882 2.288778 _IageXmal~55 | -.5924098 .3933743 -1.51 0.132 -1.363409 .1785897 _IageXmal~60 | -.8886688 .3881041 -2.29 0.022 -1.649339 -.1279989 _IageXmal~80 | -.994868 .3734877 -2.66 0.008 -1.72689 -.2628454 _IageXmal~81 | -1.40099 .4590461 -3.05 0.002 -2.300704 -.501276 _Ibmi_gr_2 | .1929941 .0849164 2.27 0.023 .0265609 .3594273 _Ibmi_gr_3 | .334175 .0814824 4.10 0.000 .1744724 .4938776 _Ibmi_gr_4 | .5230984 .0809496 6.46 0.000 .3644401 .6817566 _Iscl_gr_225 | .192923 .0843228 2.29 0.022 .0276532 .3581927 _Iscl_gr_255 | .5262667 .0810581 6.49 0.000 .3673957 .6851377 _Iscl_gr_256 | .6128653 .0814661 7.52 0.000 .4531947 .7725359 _cons | -7.340656 .3392161 -21.64 0.000 -8.005507 -6.675804 pt_yrs | (exposure) ------------------------------------------------------------------------------ . lrtest age_male_interact_bmi . Likelihood-ratio test LR chi2(3) = 80.05 (Assumption: age_male_int~i nested in .) Prob > chi2 = 0.0000 . estimates store age_male_interact_bmi_scl . * . * Adjust estimates for BMI, serum cholesterol and . * diastolic blood pressure . * . xi: poisson chd_cnt i.age_gr2*male i.bmi_gr i.scl_gr i.dbp_gr /// > , exposure(pt_yrs) irr i.age_gr2 _Iage_gr2_45-81 (naturally coded; _Iage_gr2_45 omitted) i.age_gr2*male _IageXmale_# (coded as above) i.bmi_gr _Ibmi_gr_1-4 (_Ibmi_gr_1 for bmi~r==22.79999923706055 omitted) i.scl_gr _Iscl_gr_197-256 (naturally coded; _Iscl_gr_197 omitted) i.dbp_gr _Idbp_gr_74-91 (naturally coded; _Idbp_gr_74 omitted) Iteration 0: log likelihood = -1438.3938 Iteration 1: log likelihood = -1436.7883 Iteration 2: log likelihood = -1436.7742 Iteration 3: log likelihood = -1436.7742 Poisson regression Number of obs = 1134 LR chi2(18) = 873.98 Prob > chi2 = 0.0000 Log likelihood = -1436.7742 Pseudo R2 = 0.2332 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Iage_gr2_55 | 3.757542 1.330346 3.74 0.000 1.877322 7.520886 _Iage_gr2_60 | 8.411822 2.926015 6.12 0.000 4.254057 16.63324 _Iage_gr2_80 | 12.78982 4.320504 7.54 0.000 6.596625 24.79746 _Iage_gr2_81 | 23.92786 8.701239 8.73 0.000 11.73192 48.80213 male | 4.63766 1.703033 4.18 0.000 2.257991 9.525233 _IageXmal~55 | .5610104 .2207002 -1.47 0.142 .2594838 1.212918 _IageXmal~60 | .4230948 .1642325 -2.22 0.027 .1977093 .9054161 _IageXmal~80 | .3851574 .1438922 -2.55 0.011 .1851975 .8010164 _IageXmal~81 | .2688894 .1234925 -2.86 0.004 .1093058 .6614605 _Ibmi_gr_2 | 1.159495 .0991218 1.73 0.083 .9806235 1.370994 _Ibmi_gr_3 | 1.298532 .1077862 3.15 0.002 1.103564 1.527944 _Ibmi_gr_4 | 1.479603 .1251218 4.63 0.000 1.253614 1.746332 _Iscl_gr_225 | 1.189835 .1004557 2.06 0.040 1.008374 1.403952 _Iscl_gr_255 | 1.649807 .1339827 6.16 0.000 1.407039 1.934462 _Iscl_gr_256 | 1.793581 .1466507 7.15 0.000 1.527999 2.105323 _Idbp_gr_80 | 1.18517 .0962869 2.09 0.037 1.010709 1.389744 _Idbp_gr_90 | 1.122983 .0892217 1.46 0.144 .9610473 1.312205 _Idbp_gr_91 | 1.638383 .1302205 6.21 0.000 1.402041 1.914564 pt_yrs | (exposure) ------------------------------------------------------------------------------ . lincom male + _IageXmale_55, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_55 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 2.601775 .3722797 6.68 0.000 1.965505 3.444019 ------------------------------------------------------------------------------ . lincom male + _IageXmale_60, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_60 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 1.96217 .2491985 5.31 0.000 1.529793 2.516752 ------------------------------------------------------------------------------ . lincom male + _IageXmale_80, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_80 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 1.786229 .123734 8.37 0.000 1.559457 2.045977 ------------------------------------------------------------------------------ . lincom male + _IageXmale_81, irr ( 1) [chd_cnt]male + [chd_cnt]_IageXmale_81 = 0 ------------------------------------------------------------------------------ chd_cnt | IRR Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | 1.247017 .3433152 0.80 0.423 .7269909 2.139026 ------------------------------------------------------------------------------ . lrtest age_male_interact_bmi_scl Likelihood-ratio test LR chi2(3) = 46.88 (Assumption: age_male_int~l nested in .) Prob > chi2 = 0.0000 . * . * Compress data set for residual plot . * . sort male bmi_gr scl_gr dbp_gr age_gr2 . collapse (sum) pt_yrs=pt_yrs chd_cnt=chd_cnt /// > , by (male bmi_gr scl_gr dbp_gr age_gr2) . * . * Re-analyze model (9.24) . * . xi: glm chd_cnt i.age_gr2*male i.bmi_gr i.scl_gr i.dbp_gr /// > , family(poisson) link(log) lnoffset(pt_yrs) i.age_gr2 _Iage_gr2_45-81 (naturally coded; _Iage_gr2_45 omitted) i.age_gr2*male _IageXmale_# (coded as above) i.bmi_gr _Ibmi_gr_1-4 (_Ibmi_gr_1 for bmi~r==22.79999923706055 omitted) i.scl_gr _Iscl_gr_197-256 (naturally coded; _Iscl_gr_197 omitted) i.dbp_gr _Idbp_gr_74-91 (naturally coded; _Idbp_gr_74 omitted) Iteration 0: log likelihood = -930.67703 Iteration 1: log likelihood = -874.87994 Iteration 2: log likelihood = -872.67922 Iteration 3: log likelihood = -872.64595 Iteration 4: log likelihood = -872.64595 Generalized linear models No. of obs = 623 Optimization : ML Residual df = 604 Scale parameter = 1 Deviance = 600.7760472 (1/df) Deviance = .9946623 Pearson = 633.8816072 (1/df) Pearson = 1.049473 Variance function: V(u) = u [Poisson] Link function : g(u) = ln(u) [Log] AIC = 2.862427 Log likelihood = -872.645946 BIC = -3285.69 ------------------------------------------------------------------------------ | OIM chd_cnt | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Iage_gr2_55 | 1.323765 .3540467 3.74 0.000 .6298461 2.017684 _Iage_gr2_60 | 2.129638 .3478456 6.12 0.000 1.447873 2.811403 _Iage_gr2_80 | 2.54865 .337808 7.54 0.000 1.886558 3.210741 _Iage_gr2_81 | 3.175043 .3636446 8.73 0.000 2.462313 3.887774 male | 1.53421 .3672182 4.18 0.000 .8144752 2.253944 _IageXmal~55 | -.5780157 .3933976 -1.47 0.142 -1.349061 .1930294 _IageXmal~60 | -.8601589 .3881695 -2.22 0.027 -1.620957 -.0993605 _IageXmal~80 | -.954103 .3735932 -2.55 0.011 -1.686332 -.2218737 _IageXmal~81 | -1.313455 .4592688 -2.86 0.004 -2.213605 -.4133048 _Ibmi_gr_2 | .1479847 .085487 1.73 0.083 -.0195667 .3155362 _Ibmi_gr_3 | .261234 .0830063 3.15 0.002 .0985448 .4239233 _Ibmi_gr_4 | .3917741 .0845644 4.63 0.000 .2260309 .5575174 _Iscl_gr_225 | .1738151 .0844282 2.06 0.040 .0083387 .3392914 _Iscl_gr_255 | .5006586 .0812111 6.16 0.000 .3414877 .6598295 _Iscl_gr_256 | .584214 .0817642 7.15 0.000 .4239591 .7444689 _Idbp_gr_80 | .1698858 .0812431 2.09 0.037 .0106522 .3291194 _Idbp_gr_90 | .1159887 .0794506 1.46 0.144 -.0397316 .2717091 _Idbp_gr_91 | .4937095 .0794811 6.21 0.000 .3379293 .6494897 _cons | -7.398838 .3407832 -21.71 0.000 -8.066761 -6.730916 pt_yrs | (exposure) ------------------------------------------------------------------------------ . * . * Estimate the expected number of CHD events and the . * standardized deviance residual for each record in the data set. . * . predict e_chd, mu (82 missing values generated) . predict dev, standardized deviance (82 missing values generated) . generate e_rate = 1000*e_chd/pt_yrs (82 missing values generated) . label variable e_rate "Expected incidence of CHD per thousand person-years" . * . * Draw scatterplot of the standardized deviance residual versus the . * estimated incidence of CHD. Include lowess regression curve on this plot. . * . lowess dev e_rate, bwidth(0.2) color(gray) symbol (Oh)ylabel(-3(1)4) /// > ytick(-3(0.5)4) lineopts(color(red) lwidth(medthick)) yline(-2 0 2) /// > xlabel(0(10)80) xmtick(0(2)80) . log close log: C:\MyDocs\MPH\Text\SecondEdition\WebDoFiles\9.3.Framingham.log log type: text closed on: 3 Jan 2008, 17:41:13 -----------------------------------------------------------------------------------------------