proc glmselect data=sashelp.cars plots=all; class Origin(param=ref ref='Europe' split) Cylinders(param=ordinal); model MSRP=Origin Cylinders MPG_City MPG_Highway Weight Wheelbase Length /selection=forward; run; data trial; input Trt $ Gender $ Age Dose Y $ @@; datalines; P F 68 1 No B M 74 16 No P F 67 30 No P M 66 26 Yes B F 67 28 No B F 77 16 No A F 71 12 No B F 72 50 No B F 76 9 Yes A M 71 17 Yes A F 63 27 No A F 69 18 Yes B F 66 12 No A M 62 42 No P F 64 1 Yes A F 64 17 No P M 74 4 No A F 72 25 No P M 70 1 Yes B M 66 19 No B M 59 29 No A F 64 30 No A M 70 28 No A M 69 1 No B F 78 1 No P M 83 1 Yes B F 69 42 No B M 75 30 Yes P M 77 29 Yes P F 79 20 Yes A M 70 12 No A F 69 12 No B F 65 14 No B M 70 1 No B M 67 23 No A M 76 25 Yes P M 78 12 Yes B M 77 1 Yes B F 69 24 No P M 66 4 Yes P F 65 29 No P M 60 26 Yes A M 78 15 Yes B M 75 21 Yes A F 67 11 No P F 72 27 No P F 70 13 Yes A M 75 6 Yes B F 65 7 No P F 68 27 Yes P M 68 11 Yes P M 67 17 Yes B M 70 22 No A M 65 15 No P F 67 1 Yes A M 67 10 No P F 72 11 Yes A F 74 1 No B M 80 21 Yes A F 69 3 No ; PROC LOGISTIC DATA=trial PLOTS=all; CLASS trt gender Y; MODEL y(EVENT='Yes')= trt|age|dose; STRATA gender; RUN; data gas; input state affluence spend; datalines; 1 84.9278 0.09480 2 56.6979 0.48784 3 81.8640 0.04742 3 81.6557 0.04946 3 82.9491 0.05669 3 56.4912 0.14023 3 43.9401 0.06682 4 51.1889 0.46421 4 48.9796 0.40510 4 45.2729 0.24639 4 85.1834 0.21552 4 51.9527 0.45444 5 78.3606 0.34360 5 31.1604 0.24766 5 50.1734 0.19430 5 59.1846 0.28623 5 67.4891 0.32543 5 80.1158 0.29106 6 46.1715 0.11515 6 79.1110 0.11562 6 61.9244 0.07572 6 53.8846 0.23555 6 37.2147 0.13160 6 33.0049 0.46002 6 35.4731 0.13635 6 64.9083 0.25404 6 70.9825 0.10059 7 40.9434 0.31736 7 56.5005 0.33179 7 62.0126 0.41173 7 76.9229 0.22565 7 34.9005 0.26339 7 83.2900 0.32330 7 51.7372 0.37292 8 67.4945 0.39499 8 85.8477 0.32127 8 52.6328 0.21220 9 49.0708 0.60206 9 58.2550 0.56294 9 31.0141 0.77285 9 32.0253 0.69084 9 73.1854 0.52463 9 69.2635 0.50765 9 46.8110 0.61594 9 82.0449 0.51397 10 67.5628 0.28218 10 25.8817 0.44415 11 63.6679 0.41645 12 77.6995 0.23915 12 79.5738 0.07719 12 37.8692 0.30031 12 32.2730 0.49414 12 52.7683 0.20366 12 49.8398 0.45457 12 60.6144 0.39945 12 48.5225 0.27251 12 73.5878 0.30932 13 54.5442 0.24002 13 75.5774 0.38842 13 50.5270 0.33472 13 39.8459 0.54480 14 57.2073 0.59862 14 45.9838 0.58898 14 81.3659 0.29601 14 84.4746 0.15822 14 35.6774 0.34527 14 49.3595 0.39532 14 71.7349 0.53277 14 33.0046 0.43460 15 42.9486 0.34029 15 78.5017 0.20855 15 41.9945 0.47731 15 76.4091 0.17437 16 34.1597 0.21389 16 76.7606 0.08357 16 57.4766 0.16195 16 77.9741 0.23778 16 27.8674 0.35754 16 39.3443 0.35122 17 30.0967 0.38221 17 34.8033 0.33965 17 47.9738 0.42721 17 50.4385 0.32986 17 65.7575 0.29112 17 71.8600 0.32021 17 60.9756 0.30673 17 81.8146 0.32776 17 52.8345 0.48980 18 34.3663 0.42894 18 85.5760 0.46802 18 36.7143 0.60882 18 72.8786 0.32107 18 46.1914 0.44547 18 62.9440 0.29252 19 38.7896 0.61689 20 25.1078 0.41785 20 83.8551 0.08373 20 37.9585 0.62809 20 67.9012 0.27260 20 47.0205 0.51467 ; run; PROC GLIMMIX DATA=gas METHOD=quad; CLASS state; MODEL spend = affluence / DIST=beta LINK=logit; RANDOM intercept / SUBJECT=state; RUN; data methadone; input @1 PatientID $3. @4 Time 4. @9 Clinic 1. @11 Dose 3. @15 Prison 1. @17 DrinkStart 4. @22 Status 1.; datalines; 1 428 1 50 0 . 1 2 275 1 55 1 200 1 3 262 1 55 0 . 1 4 183 1 30 0 45 1 5 259 1 65 1 . 1 6 714 1 55 0 . 1 7 438 1 65 1 198 1 8 796 1 60 1 . 0 9 892 1 50 0 590 1 10 393 1 65 1 . 1 11 161 1 80 1 77 0 12 836 1 60 1 . 1 13 523 1 55 0 414 1 14 612 1 70 0 . 1 15 212 1 60 1 . 1 16 399 1 60 1 . 1 17 771 1 75 1 . 1 18 514 1 80 1 . 1 19 512 1 80 0 . 1 21 624 1 80 1 . 1 22 209 1 60 1 184 1 23 341 1 60 1 . 1 24 299 1 55 0 102 1 25 826 1 80 0 . 0 26 262 1 65 1 . 1 27 566 1 45 1 . 0 28 368 1 55 1 . 1 30 302 1 50 1 0 1 31 602 1 60 0 . 0 32 652 1 80 0 . 1 33 293 1 65 0 165 1 34 564 1 60 0 462 0 36 394 1 55 1 273 1 37 755 1 65 1 . 1 38 591 1 55 0 387 1 39 787 1 80 0 . 0 40 739 1 60 0 . 1 41 550 1 60 1 375 1 42 837 1 60 0 405 1 43 612 1 65 0 . 1 44 581 1 70 0 . 0 45 523 1 60 0 71 1 46 504 1 60 1 . 1 48 785 1 80 1 52 1 49 774 1 65 1 179 1 50 560 1 65 0 . 1 51 160 1 35 0 . 1 52 482 1 30 0 . 1 53 518 1 65 0 . 1 54 683 1 50 0 . 1 55 147 1 65 0 62 1 57 563 1 70 1 73 1 58 646 1 60 1 . 1 59 899 1 60 0 11 1 60 857 1 60 0 . 1 61 180 1 70 1 133 1 62 452 1 60 0 . 1 63 760 1 60 0 . 1 64 496 1 65 0 . 1 65 258 1 40 1 . 1 66 181 1 60 1 . 1 67 386 1 60 0 . 1 68 439 1 80 0 305 0 69 563 1 75 0 428 0 70 337 1 65 0 . 1 71 613 1 60 1 . 0 72 192 1 80 1 . 1 73 405 1 80 0 . 0 74 667 1 50 0 . 1 75 905 1 80 0 381 0 76 247 1 70 0 . 1 77 821 1 80 0 . 1 78 821 1 75 1 . 1 79 517 1 45 0 . 0 80 346 1 60 1 . 0 81 294 1 65 0 . 1 82 244 1 60 1 16 1 83 95 1 60 1 . 1 84 376 1 55 1 . 1 85 212 1 40 0 117 1 86 96 1 70 0 . 1 87 532 1 80 0 . 1 88 522 1 70 1 . 1 89 679 1 35 0 . 1 90 408 1 50 0 . 0 91 840 1 80 0 . 0 92 148 1 65 1 119 0 93 168 1 65 0 . 1 94 489 1 80 0 . 1 95 541 1 80 0 206 0 96 205 1 50 0 . 1 97 475 1 75 1 . 0 98 237 1 45 0 . 1 99 517 1 70 0 . 1 100 749 1 70 0 174 1 101 150 1 80 1 . 1 102 465 1 65 0 . 1 103 708 2 60 1 578 1 104 713 2 50 0 436 0 105 146 2 50 0 . 0 106 450 2 55 0 . 1 109 555 2 80 0 . 0 110 460 2 50 0 . 1 111 53 2 60 1 . 0 113 122 2 60 1 89 1 114 35 2 40 1 . 1 118 532 2 70 0 . 0 119 684 2 65 0 . 0 120 769 2 70 1 . 0 121 591 2 70 0 . 0 122 769 2 40 1 . 0 123 609 2 100 1 . 0 124 932 2 80 1 . 0 125 932 2 80 1 . 0 126 587 2 110 0 . 0 127 26 2 40 0 . 1 128 72 2 40 1 . 0 129 641 2 70 0 527 0 131 367 2 70 0 . 0 132 633 2 70 0 . 0 133 661 2 40 0 448 1 134 232 2 70 1 . 1 135 13 2 60 1 3 1 137 563 2 70 0 . 0 138 969 2 80 0 509 0 1431052 2 80 0 . 0 144 944 2 80 1 . 0 145 881 2 80 0 . 0 146 190 2 50 1 . 1 148 79 2 40 0 . 1 149 884 2 50 1 . 0 150 170 2 40 0 . 1 153 286 2 45 0 . 1 156 358 2 60 0 . 0 158 326 2 60 1 . 0 159 769 2 40 1 . 0 160 161 2 40 0 . 1 161 564 2 80 1 467 0 162 268 2 70 1 . 1 163 611 2 40 1 . 0 164 322 2 55 0 225 1 1651076 2 80 1 . 0 166 2 2 40 1 0 0 168 788 2 70 0 . 0 169 575 2 80 0 265 0 170 109 2 70 1 . 1 171 730 2 80 1 . 0 172 790 2 90 0 . 0 173 456 2 70 1 . 0 175 231 2 60 1 115 1 176 143 2 70 1 . 1 177 86 2 40 1 . 0 1781021 2 80 0 . 0 179 684 2 80 1 . 0 180 878 2 60 1 . 1 181 216 2 100 0 . 1 182 808 2 60 0 . 0 183 268 2 40 1 . 1 184 222 2 40 0 . 0 186 683 2 100 0 . 0 187 496 2 40 0 . 0 188 389 2 55 0 242 1 189 126 1 75 1 19 1 190 17 1 40 1 . 1 192 350 1 60 0 . 1 193 531 2 65 1 . 0 194 317 1 50 1 103 0 195 461 1 75 1 248 0 196 37 1 60 0 28 1 197 167 1 55 1 . 1 198 358 1 45 0 42 1 199 49 1 60 0 . 1 200 457 1 40 1 . 1 201 127 1 20 0 . 1 202 7 1 40 1 . 1 203 29 1 60 1 . 1 204 62 1 40 0 58 1 205 150 1 60 1 . 0 206 223 1 40 1 . 1 207 129 1 40 1 101 0 208 204 1 65 1 . 0 209 129 1 50 1 . 1 210 581 1 65 0 . 1 211 176 1 55 0 . 1 212 30 1 60 0 8 1 213 41 1 60 0 . 1 214 543 1 40 0 . 0 215 210 1 50 1 . 0 216 193 1 70 1 0 1 217 434 1 55 0 . 1 218 367 1 45 0 . 1 219 348 1 60 1 . 1 220 28 1 50 0 . 0 221 337 1 40 0 223 0 222 175 1 60 1 145 0 223 149 2 80 1 . 1 224 546 1 50 1 . 1 225 84 1 45 0 . 1 226 283 1 80 1 210 0 227 533 1 55 0 . 1 228 207 1 50 1 169 1 229 216 1 50 0 . 1 230 28 1 50 0 14 0 231 67 1 50 1 63 1 232 62 1 60 1 . 0 233 111 1 55 0 . 0 234 257 1 60 1 . 1 235 136 1 55 1 . 1 236 342 1 60 0 . 0 237 41 2 40 0 15 1 238 531 2 45 1 . 0 239 98 1 40 0 . 0 240 145 1 55 1 . 1 241 50 1 50 0 9 1 242 53 1 50 0 . 0 243 103 1 50 1 . 0 244 2 1 60 1 . 0 245 157 1 60 1 . 1 246 75 1 55 1 63 1 247 19 1 40 1 . 1 248 35 1 60 0 . 1 249 394 2 80 1 . 0 250 117 1 40 0 . 1 251 175 1 60 1 133 1 252 180 1 60 1 . 1 253 314 1 70 0 149 1 254 480 1 50 0 . 0 255 325 1 60 1 . 0 256 280 2 90 0 94 1 257 204 1 50 0 . 1 258 366 2 55 0 . 1 259 531 2 50 1 . 0 260 59 1 45 1 . 1 261 33 1 60 1 . 1 262 540 2 80 0 . 1 263 551 2 65 0 159 0 264 90 1 40 0 . 1 266 47 1 45 0 21 1 ; run; PROC PHREG DATA=methadone; MODEL time*status(0)= dose prison Clin_Int1 Clin_Int2; Clin_Int1=Clinic*(time lt 366); Clin_Int2=Clinic*(366 le time); LABEL Clin_Int1="Clinic 2 vs 1 Effect, 1st year" Clin_Int2="Clinic 2 vs 1 Effect, 2nd year or later"; RUN;