Program 10.1+10.2. OpenBUGS code for the Normal Model Va model { for(i in 1:N){ for(j in 1:5){ bdi[ 5*(i-1) + j] ~ dnorm(mu[subject[ 5*(i-1) + j], j], tau) mu[subject[ 5*(i-1) + j], j] <- a0 + b0[subject[ 5*(i-1) + j]] + b1[subject[ 5*(i-1) + j]] * post[j] + beta1*treatment[ 5*(i-1) + j] + beta2*post[j] + beta3*treatment[ 5*(i-1) + j]*post[j] + beta.drug * (drug[ 5*(i-1) + j] - drug.bar) + beta.length*(length[ 5*(i-1) + j] - length.bar) } } for (i in 1:N){ b0[i] ~ dnorm(0.0, tau.b0) b1[i] ~ dnorm(0.0, tau.b1) } drug.bar <- mean(drug[]) length.bar<-mean(length[]) # priors: a0 ~ dnorm(0.0,1.0E-4) beta1 ~ dnorm(0.0,1.0E-4) beta2 ~ dnorm(0.0,1.0E-4) beta3 ~ dnorm(0.0,1.0E-4) beta.drug ~ dnorm(0.0,1.0E-4) beta.length ~ dnorm(0.0,1.0E-4) tau.b0 ~ dgamma(1.0E-3,1.0E-3) sigma2.b0 <- 1.0/ tau.b0 tau.b1 ~ dgamma(1.0E-3,1.0E-3) sigma2.b1 <- 1.0/ tau.b1 tau ~ dgamma(1.0E-3,1.0E-3) sigma2 <- 1.0/ tau beta0<- a0 - drug.bar*beta.drug - beta.length*length.bar } # Initial values list(tau.b0 =1, tau.b1=1, a0=0, beta1=0, beta2=0, beta3=0, beta.drug=0, beta.length=0,tau=1) list(tau.b0 =2, tau.b1=2, a0=1, beta1=1, beta2=2, beta3=1, beta.drug=-1, beta.length= -1,tau=2) # Data (shown are for the first two patients) list(N=100, post=c(0,1,1,1,1)) no[] subject[] drug[] length[] treatment[] visit[] bdi[] 1 1 0 1 0 0 29 2 1 0 1 0 2 2 3 1 0 1 0 3 2 4 1 0 1 0 5 NA 5 1 0 1 0 8 NA 6 2 1 1 1 0 32 7 2 1 1 1 2 16 8 2 1 1 1 3 24 9 2 1 1 1 5 17 10 2 1 1 1 8 20 11 3 1 0 0 0 25 12 3 1 0 0 2 20 13 3 1 0 0 3 NA 14 3 1 0 0 5 NA 15 3 1 0 0 8 NA 16 4 0 1 1 0 21 17 4 0 1 1 2 17 18 4 0 1 1 3 16 19 4 0 1 1 5 10 20 4 0 1 1 8 9 21 5 1 1 1 0 26 22 5 1 1 1 2 23 23 5 1 1 1 3 NA 24 5 1 1 1 5 NA 25 5 1 1 1 8 NA 26 6 1 0 1 0 7 27 6 1 0 1 2 0 28 6 1 0 1 3 0 29 6 1 0 1 5 0 30 6 1 0 1 8 0 31 7 1 0 0 0 17 32 7 1 0 0 2 7 33 7 1 0 0 3 7 34 7 1 0 0 5 3 35 7 1 0 0 8 7 36 8 0 1 0 0 20 37 8 0 1 0 2 20 38 8 0 1 0 3 21 39 8 0 1 0 5 19 40 8 0 1 0 8 13 41 9 1 0 1 0 18 42 9 1 0 1 2 13 43 9 1 0 1 3 14 44 9 1 0 1 5 20 45 9 1 0 1 8 11 46 10 1 1 1 0 20 47 10 1 1 1 2 5 48 10 1 1 1 3 5 49 10 1 1 1 5 8 50 10 1 1 1 8 12 51 11 0 1 0 0 30 52 11 0 1 0 2 32 53 11 0 1 0 3 24 54 11 0 1 0 5 12 55 11 0 1 0 8 2 56 12 1 0 1 0 49 57 12 1 0 1 2 35 58 12 1 0 1 3 NA 59 12 1 0 1 5 NA 60 12 1 0 1 8 NA 61 13 0 1 0 0 26 62 13 0 1 0 2 27 63 13 0 1 0 3 23 64 13 0 1 0 5 NA 65 13 0 1 0 8 NA 66 14 1 1 0 0 30 67 14 1 1 0 2 26 68 14 1 1 0 3 36 69 14 1 1 0 5 27 70 14 1 1 0 8 22 71 15 1 1 1 0 23 72 15 1 1 1 2 13 73 15 1 1 1 3 13 74 15 1 1 1 5 12 75 15 1 1 1 8 23 76 16 0 0 0 0 16 77 16 0 0 0 2 13 78 16 0 0 0 3 3 79 16 0 0 0 5 2 80 16 0 0 0 8 0 81 17 0 1 1 0 30 82 17 0 1 1 2 30 83 17 0 1 1 3 29 84 17 0 1 1 5 NA 85 17 0 1 1 8 NA 86 18 0 0 1 0 13 87 18 0 0 1 2 8 88 18 0 0 1 3 8 89 18 0 0 1 5 7 90 18 0 0 1 8 6 91 19 0 1 0 0 37 92 19 0 1 0 2 30 93 19 0 1 0 3 33 94 19 0 1 0 5 31 95 19 0 1 0 8 22 96 20 1 0 1 0 35 97 20 1 0 1 2 12 98 20 1 0 1 3 10 99 20 1 0 1 5 8 100 20 1 0 1 8 10 101 21 0 1 1 0 21 102 21 0 1 1 2 6 103 21 0 1 1 3 NA 104 21 0 1 1 5 NA 105 21 0 1 1 8 NA 106 22 0 0 0 0 26 107 22 0 0 0 2 17 108 22 0 0 0 3 17 109 22 0 0 0 5 20 110 22 0 0 0 8 12 111 23 0 1 0 0 29 112 23 0 1 0 2 22 113 23 0 1 0 3 10 114 23 0 1 0 5 NA 115 23 0 1 0 8 NA 116 24 0 1 0 0 20 117 24 0 1 0 2 21 118 24 0 1 0 3 NA 119 24 0 1 0 5 NA 120 24 0 1 0 8 NA 121 25 0 1 0 0 33 122 25 0 1 0 2 23 123 25 0 1 0 3 NA 124 25 0 1 0 5 NA 125 25 0 1 0 8 NA 126 26 0 1 1 0 19 127 26 0 1 1 2 12 128 26 0 1 1 3 13 129 26 0 1 1 5 NA 130 26 0 1 1 8 NA 131 27 1 0 0 0 12 132 27 1 0 0 2 15 133 27 1 0 0 3 NA 134 27 1 0 0 5 NA 135 27 1 0 0 8 NA 136 28 1 1 0 0 47 137 28 1 1 0 2 36 138 28 1 1 0 3 49 139 28 1 1 0 5 34 140 28 1 1 0 8 NA 141 29 1 1 1 0 36 142 29 1 1 1 2 6 143 29 1 1 1 3 0 144 29 1 1 1 5 0 145 29 1 1 1 8 2 146 30 0 0 1 0 10 147 30 0 0 1 2 8 148 30 0 0 1 3 6 149 30 0 0 1 5 3 150 30 0 0 1 8 3 151 31 0 0 0 0 27 152 31 0 0 0 2 7 153 31 0 0 0 3 15 154 31 0 0 0 5 16 155 31 0 0 0 8 0 156 32 0 0 1 0 18 157 32 0 0 1 2 10 158 32 0 0 1 3 10 159 32 0 0 1 5 6 160 32 0 0 1 8 8 161 33 1 0 1 0 11 162 33 1 0 1 2 8 163 33 1 0 1 3 3 164 33 1 0 1 5 2 165 33 1 0 1 8 15 166 34 1 0 1 0 6 167 34 1 0 1 2 7 168 34 1 0 1 3 NA 169 34 1 0 1 5 NA 170 34 1 0 1 8 NA 171 35 1 1 1 0 44 172 35 1 1 1 2 24 173 35 1 1 1 3 20 174 35 1 1 1 5 29 175 35 1 1 1 8 14 176 36 0 0 0 0 38 177 36 0 0 0 2 38 178 36 0 0 0 3 NA 179 36 0 0 0 5 NA 180 36 0 0 0 8 NA 181 37 0 0 0 0 21 182 37 0 0 0 2 14 183 37 0 0 0 3 20 184 37 0 0 0 5 1 185 37 0 0 0 8 8 186 38 1 1 0 0 34 187 38 1 1 0 2 17 188 38 1 1 0 3 8 189 38 1 1 0 5 9 190 38 1 1 0 8 13 191 39 1 0 1 0 9 192 39 1 0 1 2 7 193 39 1 0 1 3 1 194 39 1 0 1 5 NA 195 39 1 0 1 8 NA 196 40 1 1 0 0 38 197 40 1 1 0 2 27 198 40 1 1 0 3 19 199 40 1 1 0 5 20 200 40 1 1 0 8 30 201 41 1 0 1 0 46 202 41 1 0 1 2 40 203 41 1 0 1 3 NA 204 41 1 0 1 5 NA 205 41 1 0 1 8 NA 206 42 0 0 0 0 20 207 42 0 0 0 2 19 208 42 0 0 0 3 18 209 42 0 0 0 5 19 210 42 0 0 0 8 18 211 43 1 1 0 0 17 212 43 1 1 0 2 29 213 43 1 1 0 3 2 214 43 1 1 0 5 0 215 43 1 1 0 8 0 216 44 0 1 1 0 18 217 44 0 1 1 2 20 218 44 0 1 1 3 NA 219 44 0 1 1 5 NA 220 44 0 1 1 8 NA 221 45 1 1 1 0 42 222 45 1 1 1 2 1 223 45 1 1 1 3 8 224 45 1 1 1 5 10 225 45 1 1 1 8 6 226 46 0 0 1 0 30 227 46 0 0 1 2 30 228 46 0 0 1 3 NA 229 46 0 0 1 5 NA 230 46 0 0 1 8 NA 231 47 1 0 1 0 33 232 47 1 0 1 2 27 233 47 1 0 1 3 16 234 47 1 0 1 5 30 235 47 1 0 1 8 15 236 48 0 0 1 0 12 237 48 0 0 1 2 1 238 48 0 0 1 3 0 239 48 0 0 1 5 0 240 48 0 0 1 8 NA 241 49 1 0 1 0 2 242 49 1 0 1 2 5 243 49 1 0 1 3 NA 244 49 1 0 1 5 NA 245 49 1 0 1 8 NA 246 50 0 1 0 0 36 247 50 0 1 0 2 42 248 50 0 1 0 3 49 249 50 0 1 0 5 47 250 50 0 1 0 8 40 251 51 0 0 0 0 35 252 51 0 0 0 2 30 253 51 0 0 0 3 NA 254 51 0 0 0 5 NA 255 51 0 0 0 8 NA 256 52 0 0 1 0 23 257 52 0 0 1 2 20 258 52 0 0 1 3 NA 259 52 0 0 1 5 NA 260 52 0 0 1 8 NA 261 53 0 1 0 0 31 262 53 0 1 0 2 48 263 53 0 1 0 3 38 264 53 0 1 0 5 38 265 53 0 1 0 8 37 266 54 1 0 1 0 8 267 54 1 0 1 2 5 268 54 1 0 1 3 7 269 54 1 0 1 5 NA 270 54 1 0 1 8 NA 271 55 1 0 0 0 23 272 55 1 0 0 2 21 273 55 1 0 0 3 26 274 55 1 0 0 5 NA 275 55 1 0 0 8 NA 276 56 1 0 1 0 7 277 56 1 0 1 2 7 278 56 1 0 1 3 5 279 56 1 0 1 5 4 280 56 1 0 1 8 0 281 57 0 0 0 0 14 282 57 0 0 0 2 13 283 57 0 0 0 3 14 284 57 0 0 0 5 NA 285 57 0 0 0 8 NA 286 58 0 0 0 0 40 287 58 0 0 0 2 36 288 58 0 0 0 3 33 289 58 0 0 0 5 NA 290 58 0 0 0 8 NA 291 59 1 0 1 0 23 292 59 1 0 1 2 30 293 59 1 0 1 3 NA 294 59 1 0 1 5 NA 295 59 1 0 1 8 NA 296 60 0 1 1 0 14 297 60 0 1 1 2 3 298 60 0 1 1 3 NA 299 60 0 1 1 5 NA 300 60 0 1 1 8 NA 301 61 0 1 0 0 22 302 61 0 1 0 2 20 303 61 0 1 0 3 16 304 61 0 1 0 5 24 305 61 0 1 0 8 16 306 62 0 1 0 0 23 307 62 0 1 0 2 23 308 62 0 1 0 3 15 309 62 0 1 0 5 25 310 62 0 1 0 8 17 311 63 0 0 0 0 15 312 63 0 0 0 2 7 313 63 0 0 0 3 13 314 63 0 0 0 5 13 315 63 0 0 0 8 NA 316 64 0 1 0 0 8 317 64 0 1 0 2 12 318 64 0 1 0 3 11 319 64 0 1 0 5 26 320 64 0 1 0 8 NA 321 65 0 1 1 0 12 322 65 0 1 1 2 18 323 65 0 1 1 3 NA 324 65 0 1 1 5 NA 325 65 0 1 1 8 NA 326 66 0 1 0 0 7 327 66 0 1 0 2 6 328 66 0 1 0 3 2 329 66 0 1 0 5 1 330 66 0 1 0 8 NA 331 67 1 0 0 0 17 332 67 1 0 0 2 9 333 67 1 0 0 3 3 334 67 1 0 0 5 1 335 67 1 0 0 8 0 336 68 1 0 1 0 33 337 68 1 0 1 2 18 338 68 1 0 1 3 16 339 68 1 0 1 5 NA 340 68 1 0 1 8 NA 341 69 0 0 0 0 27 342 69 0 0 0 2 20 343 69 0 0 0 3 NA 344 69 0 0 0 5 NA 345 69 0 0 0 8 NA 346 70 0 0 1 0 27 347 70 0 0 1 2 30 348 70 0 0 1 3 NA 349 70 0 0 1 5 NA 350 70 0 0 1 8 NA 351 71 0 0 1 0 9 352 71 0 0 1 2 6 353 71 0 0 1 3 10 354 71 0 0 1 5 1 355 71 0 0 1 8 0 356 72 0 1 1 0 40 357 72 0 1 1 2 30 358 72 0 1 1 3 12 359 72 0 1 1 5 NA 360 72 0 1 1 8 NA 361 73 0 1 0 0 11 362 73 0 1 0 2 8 363 73 0 1 0 3 7 364 73 0 1 0 5 NA 365 73 0 1 0 8 NA 366 74 0 0 0 0 9 367 74 0 0 0 2 8 368 74 0 0 0 3 NA 369 74 0 0 0 5 NA 370 74 0 0 0 8 NA 371 75 0 1 0 0 14 372 75 0 1 0 2 22 373 75 0 1 0 3 21 374 75 0 1 0 5 24 375 75 0 1 0 8 19 376 76 1 1 1 0 28 377 76 1 1 1 2 9 378 76 1 1 1 3 20 379 76 1 1 1 5 18 380 76 1 1 1 8 13 381 77 0 1 1 0 15 382 77 0 1 1 2 9 383 77 0 1 1 3 13 384 77 0 1 1 5 14 385 77 0 1 1 8 10 386 78 1 1 1 0 22 387 78 1 1 1 2 10 388 78 1 1 1 3 5 389 78 1 1 1 5 5 390 78 1 1 1 8 12 391 79 0 0 0 0 23 392 79 0 0 0 2 9 393 79 0 0 0 3 NA 394 79 0 0 0 5 NA 395 79 0 0 0 8 NA 396 80 0 1 0 0 21 397 80 0 1 0 2 22 398 80 0 1 0 3 24 399 80 0 1 0 5 23 400 80 0 1 0 8 22 401 81 0 1 0 0 27 402 81 0 1 0 2 31 403 81 0 1 0 3 28 404 81 0 1 0 5 22 405 81 0 1 0 8 14 406 82 1 1 1 0 14 407 82 1 1 1 2 15 408 82 1 1 1 3 NA 409 82 1 1 1 5 NA 410 82 1 1 1 8 NA 411 83 0 1 0 0 10 412 83 0 1 0 2 13 413 83 0 1 0 3 12 414 83 0 1 0 5 8 415 83 0 1 0 8 20 416 84 1 0 0 0 21 417 84 1 0 0 2 9 418 84 1 0 0 3 6 419 84 1 0 0 5 7 420 84 1 0 0 8 1 421 85 1 1 1 0 46 422 85 1 1 1 2 36 423 85 1 1 1 3 53 424 85 1 1 1 5 NA 425 85 1 1 1 8 NA 426 86 0 1 1 0 36 427 86 0 1 1 2 14 428 86 0 1 1 3 7 429 86 0 1 1 5 15 430 86 0 1 1 8 15 431 87 1 1 1 0 23 432 87 1 1 1 2 17 433 87 1 1 1 3 NA 434 87 1 1 1 5 NA 435 87 1 1 1 8 NA 436 88 1 1 0 0 35 437 88 1 1 0 2 0 438 88 1 1 0 3 6 439 88 1 1 0 5 0 440 88 1 1 0 8 1 441 89 1 0 1 0 33 442 89 1 0 1 2 13 443 89 1 0 1 3 13 444 89 1 0 1 5 10 445 89 1 0 1 8 8 446 90 0 0 1 0 19 447 90 0 0 1 2 4 448 90 0 0 1 3 27 449 90 0 0 1 5 1 450 90 0 0 1 8 2 451 91 0 0 0 0 16 452 91 0 0 0 2 NA 453 91 0 0 0 3 NA 454 91 0 0 0 5 NA 455 91 0 0 0 8 NA 456 92 1 0 1 0 30 457 92 1 0 1 2 26 458 92 1 0 1 3 28 459 92 1 0 1 5 NA 460 92 1 0 1 8 NA 461 93 1 0 1 0 17 462 93 1 0 1 2 8 463 93 1 0 1 3 7 464 93 1 0 1 5 12 465 93 1 0 1 8 NA 466 94 0 1 1 0 19 467 94 0 1 1 2 4 468 94 0 1 1 3 3 469 94 0 1 1 5 3 470 94 0 1 1 8 3 471 95 0 1 1 0 16 472 95 0 1 1 2 11 473 95 0 1 1 3 4 474 95 0 1 1 5 2 475 95 0 1 1 8 3 476 96 1 1 1 0 16 477 96 1 1 1 2 16 478 96 1 1 1 3 10 479 96 1 1 1 5 10 480 96 1 1 1 8 8 481 97 1 0 0 0 28 482 97 1 0 0 2 NA 483 97 1 0 0 3 NA 484 97 1 0 0 5 NA 485 97 1 0 0 8 NA 486 98 0 1 1 0 11 487 98 0 1 1 2 22 488 98 0 1 1 3 9 489 98 0 1 1 5 11 490 98 0 1 1 8 11 491 99 0 0 0 0 13 492 99 0 0 0 2 5 493 99 0 0 0 3 5 494 99 0 0 0 5 0 495 99 0 0 0 8 6 496 100 1 0 0 0 43 497 100 1 0 0 2 NA 498 100 1 0 0 3 NA 499 100 1 0 0 5 NA 500 100 1 0 0 8 NA END Program 10.3+10.4. OpenBUGS code for the logistic Model IV model { for(i in 1:N){ for(j in 1:5){ status[ 5*(i-1) + j] ~ dbern(p[subject[ 5*(i-1) + j ], j]) # logit( p[subject[ 5*(i-1) + j], j] ) <- a0 + b0[subject[ 5*(i-1) +j] + beta1*treatment[ 5*(i-1) + j] + beta2*post[j] + beta3*treatment[ 5*(i-1) + j]*post[j] + beta.centre * centre[ 5*(i-1) + j ] + beta.gender * gender[ 5*(i-1) + j] + beta.age*(age[ 5*(i-1) + j] - age.bar) } @ } # @for (i in 1:N){ b0[i] ~ dnorm(0.0, tau.b0) } age.bar <- mean(age[]) # priors: a0 ~ dnorm(0.0,1.0E-4) beta1 ~ dnorm(0.0,1.0E-4) beta2 ~ dnorm(0.0,1.0E-4) beta3 ~ dnorm(0.0,1.0E-4) beta.centre ~ dnorm(0.0,1.0E-4) beta.gender ~ dnorm(0.0,1.0E-4) beta.age ~ dnorm(0.0,1.0E-4) tau.b0 ~ dgamma(1.0E-3,1.0E-3) sigma2.b0 <- 1.0/ tau.b0 # beta0<- a0 - age.bar*beta.age } # Initial values list(tau.b0 =1, a0=0, beta1=0, beta2=0, beta3=0, beta.centre=0, beta.gender=0, beta.age=0) list(tau.b0 =2, a0=1, beta1=1, beta2=2, beta3=1, beta.centre=-1, beta.gender= -1, beta.age=1) # Data list(N=111, post=c(0,1,1,1,1)) no[] centre[] subject[] visit[] treatment[] status[] gender[] age[] 1 1 1 0 0 0 0 46 2 1 1 1 0 0 0 46 3 1 1 2 0 0 0 46 4 1 1 3 0 0 0 46 5 1 1 4 0 0 0 46 6 1 2 0 0 0 0 28 7 1 2 1 0 0 0 28 8 1 2 2 0 0 0 28 9 1 2 3 0 0 0 28 10 1 2 4 0 0 0 28 11 1 3 0 1 1 0 23 12 1 3 1 1 1 0 23 13 1 3 2 1 1 0 23 14 1 3 3 1 1 0 23 15 1 3 4 1 1 0 23 16 1 4 0 0 1 0 44 17 1 4 1 0 1 0 44 18 1 4 2 0 1 0 44 19 1 4 3 0 1 0 44 20 1 4 4 0 0 0 44 21 1 5 0 0 1 1 13 22 1 5 1 0 1 1 13 23 1 5 2 0 1 1 13 24 1 5 3 0 1 1 13 25 1 5 4 0 1 1 13 26 1 6 0 1 0 0 34 27 1 6 1 1 0 0 34 28 1 6 2 1 0 0 34 29 1 6 3 1 0 0 34 30 1 6 4 1 0 0 34 31 1 7 0 0 0 0 43 32 1 7 1 0 1 0 43 33 1 7 2 0 0 0 43 34 1 7 3 0 1 0 43 35 1 7 4 0 1 0 43 36 1 8 0 1 0 0 28 37 1 8 1 1 0 0 28 38 1 8 2 1 0 0 28 39 1 8 3 1 0 0 28 40 1 8 4 1 0 0 28 41 1 9 0 1 1 0 31 42 1 9 1 1 1 0 31 43 1 9 2 1 1 0 31 44 1 9 3 1 1 0 31 45 1 9 4 1 1 0 31 46 1 10 0 0 1 0 37 47 1 10 1 0 0 0 37 48 1 10 2 0 1 0 37 49 1 10 3 0 1 0 37 50 1 10 4 0 0 0 37 51 1 11 0 1 1 0 30 52 1 11 1 1 1 0 30 53 1 11 2 1 1 0 30 54 1 11 3 1 1 0 30 55 1 11 4 1 1 0 30 56 1 12 0 1 0 0 14 57 1 12 1 1 1 0 14 58 1 12 2 1 1 0 14 59 1 12 3 1 1 0 14 60 1 12 4 1 0 0 14 61 1 13 0 0 1 0 23 62 1 13 1 0 1 0 23 63 1 13 2 0 0 0 23 64 1 13 3 0 0 0 23 65 1 13 4 0 0 0 23 66 1 14 0 0 0 0 30 67 1 14 1 0 0 0 30 68 1 14 2 0 0 0 30 69 1 14 3 0 0 0 30 70 1 14 4 0 0 0 30 71 1 15 0 0 1 0 20 72 1 15 1 0 1 0 20 73 1 15 2 0 1 0 20 74 1 15 3 0 1 0 20 75 1 15 4 0 1 0 20 76 1 16 0 1 0 0 22 77 1 16 1 1 0 0 22 78 1 16 2 1 0 0 22 79 1 16 3 1 0 0 22 80 1 16 4 1 1 0 22 81 1 17 0 0 0 0 25 82 1 17 1 0 0 0 25 83 1 17 2 0 0 0 25 84 1 17 3 0 0 0 25 85 1 17 4 0 0 0 25 86 1 18 0 1 0 1 47 87 1 18 1 1 0 1 47 88 1 18 2 1 1 1 47 89 1 18 3 1 1 1 47 90 1 18 4 1 1 1 47 91 1 19 0 0 0 1 31 92 1 19 1 0 0 1 31 93 1 19 2 0 0 1 31 94 1 19 3 0 0 1 31 95 1 19 4 0 0 1 31 96 1 20 0 1 1 0 20 97 1 20 1 1 1 0 20 98 1 20 2 1 0 0 20 99 1 20 3 1 1 0 20 100 1 20 4 1 0 0 20 101 1 21 0 1 0 0 26 102 1 21 1 1 1 0 26 103 1 21 2 1 0 0 26 104 1 21 3 1 1 0 26 105 1 21 4 1 0 0 26 106 1 22 0 1 1 0 46 107 1 22 1 1 1 0 46 108 1 22 2 1 1 0 46 109 1 22 3 1 1 0 46 110 1 22 4 1 1 0 46 111 1 23 0 1 1 0 32 112 1 23 1 1 1 0 32 113 1 23 2 1 1 0 32 114 1 23 3 1 1 0 32 115 1 23 4 1 1 0 32 116 1 24 0 1 0 0 48 117 1 24 1 1 1 0 48 118 1 24 2 1 0 0 48 119 1 24 3 1 0 0 48 120 1 24 4 1 0 0 48 121 1 25 0 0 0 1 35 122 1 25 1 0 0 1 35 123 1 25 2 0 0 1 35 124 1 25 3 0 0 1 35 125 1 25 4 0 0 1 35 126 1 26 0 1 0 0 26 127 1 26 1 1 0 0 26 128 1 26 2 1 0 0 26 129 1 26 3 1 0 0 26 130 1 26 4 1 0 0 26 131 1 27 0 0 1 0 23 132 1 27 1 0 1 0 23 133 1 27 2 0 0 0 23 134 1 27 3 0 1 0 23 135 1 27 4 0 1 0 23 136 1 28 0 0 0 1 36 137 1 28 1 0 1 1 36 138 1 28 2 0 1 1 36 139 1 28 3 0 0 1 36 140 1 28 4 0 0 1 36 141 1 29 0 0 0 0 19 142 1 29 1 0 1 0 19 143 1 29 2 0 1 0 19 144 1 29 3 0 0 0 19 145 1 29 4 0 0 0 19 146 1 30 0 1 0 0 28 147 1 30 1 1 0 0 28 148 1 30 2 1 0 0 28 149 1 30 3 1 0 0 28 150 1 30 4 1 0 0 28 151 1 31 0 0 0 0 37 152 1 31 1 0 0 0 37 153 1 31 2 0 0 0 37 154 1 31 3 0 0 0 37 155 1 31 4 0 0 0 37 156 1 32 0 1 0 0 23 157 1 32 1 1 1 0 23 158 1 32 2 1 1 0 23 159 1 32 3 1 1 0 23 160 1 32 4 1 1 0 23 161 1 33 0 1 1 0 30 162 1 33 1 1 1 0 30 163 1 33 2 1 1 0 30 164 1 33 3 1 1 0 30 165 1 33 4 1 0 0 30 166 1 34 0 0 0 0 15 167 1 34 1 0 0 0 15 168 1 34 2 0 1 0 15 169 1 34 3 0 1 0 15 170 1 34 4 0 0 0 15 171 1 35 0 1 0 0 26 172 1 35 1 1 0 0 26 173 1 35 2 1 0 0 26 174 1 35 3 1 1 0 26 175 1 35 4 1 0 0 26 176 1 36 0 0 0 1 45 177 1 36 1 0 0 1 45 178 1 36 2 0 0 1 45 179 1 36 3 0 0 1 45 180 1 36 4 0 0 1 45 181 1 37 0 1 0 0 31 182 1 37 1 1 0 0 31 183 1 37 2 1 1 0 31 184 1 37 3 1 0 0 31 185 1 37 4 1 0 0 31 186 1 38 0 1 0 0 50 187 1 38 1 1 0 0 50 188 1 38 2 1 0 0 50 189 1 38 3 1 0 0 50 190 1 38 4 1 0 0 50 191 1 39 0 0 0 0 28 192 1 39 1 0 0 0 28 193 1 39 2 0 0 0 28 194 1 39 3 0 0 0 28 195 1 39 4 0 0 0 28 196 1 40 0 0 0 0 26 197 1 40 1 0 0 0 26 198 1 40 2 0 0 0 26 199 1 40 3 0 0 0 26 200 1 40 4 0 0 0 26 201 1 41 0 0 0 0 14 202 1 41 1 0 0 0 14 203 1 41 2 0 0 0 14 204 1 41 3 0 0 0 14 205 1 41 4 0 1 0 14 206 1 42 0 1 0 0 31 207 1 42 1 1 0 0 31 208 1 42 2 1 1 0 31 209 1 42 3 1 0 0 31 210 1 42 4 1 0 0 31 211 1 43 0 0 1 0 13 212 1 43 1 0 1 0 13 213 1 43 2 0 1 0 13 214 1 43 3 0 1 0 13 215 1 43 4 0 1 0 13 216 1 44 0 0 0 0 27 217 1 44 1 0 0 0 27 218 1 44 2 0 0 0 27 219 1 44 3 0 0 0 27 220 1 44 4 0 0 0 27 221 1 45 0 0 0 0 26 222 1 45 1 0 1 0 26 223 1 45 2 0 0 0 26 224 1 45 3 0 1 0 26 225 1 45 4 0 1 0 26 226 1 46 0 0 0 0 49 227 1 46 1 0 0 0 49 228 1 46 2 0 0 0 49 229 1 46 3 0 0 0 49 230 1 46 4 0 0 0 49 231 1 47 0 0 0 0 63 232 1 47 1 0 0 0 63 233 1 47 2 0 0 0 63 234 1 47 3 0 0 0 63 235 1 47 4 0 0 0 63 236 1 48 0 1 1 0 57 237 1 48 1 1 1 0 57 238 1 48 2 1 1 0 57 239 1 48 3 1 1 0 57 240 1 48 4 1 1 0 57 241 1 49 0 0 1 0 27 242 1 49 1 0 1 0 27 243 1 49 2 0 1 0 27 244 1 49 3 0 1 0 27 245 1 49 4 0 1 0 27 246 1 50 0 1 0 0 22 247 1 50 1 1 0 0 22 248 1 50 2 1 1 0 22 249 1 50 3 1 1 0 22 250 1 50 4 1 1 0 22 251 1 51 0 1 0 0 15 252 1 51 1 1 0 0 15 253 1 51 2 1 1 0 15 254 1 51 3 1 1 0 15 255 1 51 4 1 1 0 15 256 1 52 0 0 0 0 43 257 1 52 1 0 0 0 43 258 1 52 2 0 0 0 43 259 1 52 3 0 1 0 43 260 1 52 4 0 0 0 43 261 1 53 0 1 0 1 32 262 1 53 1 1 0 1 32 263 1 53 2 1 0 1 32 264 1 53 3 1 1 1 32 265 1 53 4 1 0 1 32 266 1 54 0 1 1 0 11 267 1 54 1 1 1 0 11 268 1 54 2 1 1 0 11 269 1 54 3 1 1 0 11 270 1 54 4 1 0 0 11 271 1 55 0 0 1 0 24 272 1 55 1 0 1 0 24 273 1 55 2 0 1 0 24 274 1 55 3 0 1 0 24 275 1 55 4 0 1 0 24 276 1 56 0 1 0 0 25 277 1 56 1 1 1 0 25 278 1 56 2 1 1 0 25 279 1 56 3 1 0 0 25 280 1 56 4 1 1 0 25 281 2 57 0 0 0 1 39 282 2 57 1 0 0 1 39 283 2 57 2 0 0 1 39 284 2 57 3 0 0 1 39 285 2 57 4 0 0 1 39 286 2 58 0 1 0 0 25 287 2 58 1 1 0 0 25 288 2 58 2 1 1 0 25 289 2 58 3 1 1 0 25 290 2 58 4 1 1 0 25 291 2 59 0 1 1 0 58 292 2 59 1 1 1 0 58 293 2 59 2 1 1 0 58 294 2 59 3 1 1 0 58 295 2 59 4 1 1 0 58 296 2 60 0 0 1 1 51 297 2 60 1 0 1 1 51 298 2 60 2 0 0 1 51 299 2 60 3 0 1 1 51 300 2 60 4 0 1 1 51 301 2 61 0 0 1 1 32 302 2 61 1 0 0 1 32 303 2 61 2 0 0 1 32 304 2 61 3 0 1 1 32 305 2 61 4 0 1 1 32 306 2 62 0 0 1 0 45 307 2 62 1 0 1 0 45 308 2 62 2 0 0 0 45 309 2 62 3 0 0 0 45 310 2 62 4 0 0 0 45 311 2 63 0 0 1 1 44 312 2 63 1 0 1 1 44 313 2 63 2 0 1 1 44 314 2 63 3 0 1 1 44 315 2 63 4 0 1 1 44 316 2 64 0 0 0 1 48 317 2 64 1 0 0 1 48 318 2 64 2 0 0 1 48 319 2 64 3 0 0 1 48 320 2 64 4 0 0 1 48 321 2 65 0 1 0 0 26 322 2 65 1 1 1 0 26 323 2 65 2 1 1 0 26 324 2 65 3 1 1 0 26 325 2 65 4 1 1 0 26 326 2 66 0 1 0 0 14 327 2 66 1 1 1 0 14 328 2 66 2 1 1 0 14 329 2 66 3 1 1 0 14 330 2 66 4 1 1 0 14 331 2 67 0 0 0 1 48 332 2 67 1 0 0 1 48 333 2 67 2 0 0 1 48 334 2 67 3 0 0 1 48 335 2 67 4 0 0 1 48 336 2 68 0 1 1 0 13 337 2 68 1 1 1 0 13 338 2 68 2 1 1 0 13 339 2 68 3 1 1 0 13 340 2 68 4 1 1 0 13 341 2 69 0 0 0 0 20 342 2 69 1 0 1 0 20 343 2 69 2 0 1 0 20 344 2 69 3 0 1 0 20 345 2 69 4 0 1 0 20 346 2 70 0 1 1 0 37 347 2 70 1 1 1 0 37 348 2 70 2 1 0 0 37 349 2 70 3 1 0 0 37 350 2 70 4 1 1 0 37 351 2 71 0 1 1 0 25 352 2 71 1 1 1 0 25 353 2 71 2 1 1 0 25 354 2 71 3 1 1 0 25 355 2 71 4 1 1 0 25 356 2 72 0 1 0 0 20 357 2 72 1 1 0 0 20 358 2 72 2 1 0 0 20 359 2 72 3 1 0 0 20 360 2 72 4 1 0 0 20 361 2 73 0 0 0 1 58 362 2 73 1 0 1 1 58 363 2 73 2 0 0 1 58 364 2 73 3 0 0 1 58 365 2 73 4 0 0 1 58 366 2 74 0 0 1 0 38 367 2 74 1 0 1 0 38 368 2 74 2 0 0 0 38 369 2 74 3 0 0 0 38 370 2 74 4 0 0 0 38 371 2 75 0 1 1 0 55 372 2 75 1 1 1 0 55 373 2 75 2 1 1 0 55 374 2 75 3 1 1 0 55 375 2 75 4 1 1 0 55 376 2 76 0 1 1 0 24 377 2 76 1 1 1 0 24 378 2 76 2 1 1 0 24 379 2 76 3 1 1 0 24 380 2 76 4 1 1 0 24 381 2 77 0 0 1 1 36 382 2 77 1 0 1 1 36 383 2 77 2 0 0 1 36 384 2 77 3 0 0 1 36 385 2 77 4 0 1 1 36 386 2 78 0 0 0 0 36 387 2 78 1 0 1 0 36 388 2 78 2 0 1 0 36 389 2 78 3 0 1 0 36 390 2 78 4 0 1 0 36 391 2 79 0 1 1 1 60 392 2 79 1 1 1 1 60 393 2 79 2 1 1 1 60 394 2 79 3 1 1 1 60 395 2 79 4 1 1 1 60 396 2 80 0 0 1 0 15 397 2 80 1 0 0 0 15 398 2 80 2 0 0 0 15 399 2 80 3 0 1 0 15 400 2 80 4 0 1 0 15 401 2 81 0 1 1 0 25 402 2 81 1 1 1 0 25 403 2 81 2 1 1 0 25 404 2 81 3 1 1 0 25 405 2 81 4 1 0 0 25 406 2 82 0 1 1 0 35 407 2 82 1 1 1 0 35 408 2 82 2 1 1 0 35 409 2 82 3 1 1 0 35 410 2 82 4 1 1 0 35 411 2 83 0 1 1 0 19 412 2 83 1 1 1 0 19 413 2 83 2 1 0 0 19 414 2 83 3 1 1 0 19 415 2 83 4 1 1 0 19 416 2 84 0 0 1 1 31 417 2 84 1 0 1 1 31 418 2 84 2 0 1 1 31 419 2 84 3 0 1 1 31 420 2 84 4 0 1 1 31 421 2 85 0 1 1 0 21 422 2 85 1 1 1 0 21 423 2 85 2 1 1 0 21 424 2 85 3 1 1 0 21 425 2 85 4 1 1 0 21 426 2 86 0 1 0 1 37 427 2 86 1 1 1 1 37 428 2 86 2 1 1 1 37 429 2 86 3 1 1 1 37 430 2 86 4 1 1 1 37 431 2 87 0 0 0 0 52 432 2 87 1 0 1 0 52 433 2 87 2 0 1 0 52 434 2 87 3 0 1 0 52 435 2 87 4 0 1 0 52 436 2 88 0 1 0 0 55 437 2 88 1 1 0 0 55 438 2 88 2 1 1 0 55 439 2 88 3 1 1 0 55 440 2 88 4 1 0 0 55 441 2 89 0 0 1 0 19 442 2 89 1 0 0 0 19 443 2 89 2 0 0 0 19 444 2 89 3 0 1 0 19 445 2 89 4 0 1 0 19 446 2 90 0 0 1 0 20 447 2 90 1 0 0 0 20 448 2 90 2 0 1 0 20 449 2 90 3 0 1 0 20 450 2 90 4 0 1 0 20 451 2 91 0 0 1 0 42 452 2 91 1 0 0 0 42 453 2 91 2 0 0 0 42 454 2 91 3 0 0 0 42 455 2 91 4 0 0 0 42 456 2 92 0 1 1 0 41 457 2 92 1 1 1 0 41 458 2 92 2 1 1 0 41 459 2 92 3 1 1 0 41 460 2 92 4 1 1 0 41 461 2 93 0 1 0 0 52 462 2 93 1 1 0 0 52 463 2 93 2 1 0 0 52 464 2 93 3 1 0 0 52 465 2 93 4 1 0 0 52 466 2 94 0 0 0 1 47 467 2 94 1 0 1 1 47 468 2 94 2 0 1 1 47 469 2 94 3 0 0 1 47 470 2 94 4 0 1 1 47 471 2 95 0 0 1 0 11 472 2 95 1 0 1 0 11 473 2 95 2 0 1 0 11 474 2 95 3 0 1 0 11 475 2 95 4 0 1 0 11 476 2 96 0 0 0 0 14 477 2 96 1 0 0 0 14 478 2 96 2 0 0 0 14 479 2 96 3 0 1 0 14 480 2 96 4 0 0 0 14 481 2 97 0 0 1 0 15 482 2 97 1 0 1 0 15 483 2 97 2 0 1 0 15 484 2 97 3 0 1 0 15 485 2 97 4 0 1 0 15 486 2 98 0 0 1 0 66 487 2 98 1 0 1 0 66 488 2 98 2 0 1 0 66 489 2 98 3 0 1 0 66 490 2 98 4 0 1 0 66 491 2 99 0 1 0 0 34 492 2 99 1 1 1 0 34 493 2 99 2 1 1 0 34 494 2 99 3 1 0 0 34 495 2 99 4 1 1 0 34 496 2 100 0 0 0 0 43 497 2 100 1 0 0 0 43 498 2 100 2 0 0 0 43 499 2 100 3 0 0 0 43 500 2 100 4 0 0 0 43 501 2 101 0 0 1 0 33 502 2 101 1 0 1 0 33 503 2 101 2 0 1 0 33 504 2 101 3 0 0 0 33 505 2 101 4 0 1 0 33 506 2 102 0 0 1 0 48 507 2 102 1 0 1 0 48 508 2 102 2 0 0 0 48 509 2 102 3 0 0 0 48 510 2 102 4 0 0 0 48 511 2 103 0 1 0 0 20 512 2 103 1 1 1 0 20 513 2 103 2 1 1 0 20 514 2 103 3 1 1 0 20 515 2 103 4 1 1 0 20 516 2 104 0 0 1 1 39 517 2 104 1 0 0 1 39 518 2 104 2 0 1 1 39 519 2 104 3 0 0 1 39 520 2 104 4 0 0 1 39 521 2 105 0 1 0 0 28 522 2 105 1 1 1 0 28 523 2 105 2 1 0 0 28 524 2 105 3 1 0 0 28 525 2 105 4 1 0 0 28 526 2 106 0 0 0 1 38 527 2 106 1 0 0 1 38 528 2 106 2 0 0 1 38 529 2 106 3 0 0 1 38 530 2 106 4 0 0 1 38 531 2 107 0 1 1 0 43 532 2 107 1 1 1 0 43 533 2 107 2 1 1 0 43 534 2 107 3 1 1 0 43 535 2 107 4 1 1 0 43 536 2 108 0 1 0 1 39 537 2 108 1 1 1 1 39 538 2 108 2 1 1 1 39 539 2 108 3 1 1 1 39 540 2 108 4 1 1 1 39 541 2 109 0 1 0 0 68 542 2 109 1 1 1 0 68 543 2 109 2 1 1 0 68 544 2 109 3 1 1 0 68 545 2 109 4 1 1 0 68 546 2 110 0 1 1 1 63 547 2 110 1 1 1 1 63 548 2 110 2 1 1 1 63 549 2 110 3 1 1 1 63 550 2 110 4 1 1 1 63 551 2 111 0 1 1 0 31 552 2 111 1 1 1 0 31 553 2 111 2 1 1 0 31 554 2 111 3 1 1 0 31 555 2 111 4 1 1 0 31 END Program 10.5+10.6. OpenBUGS code for the Poisson Model Va model { for(i in 1 : N) { for(j in 1 : 5) { log(mu[subject[5*(i-1) + j], j]) <- log(t[j]) + a0 + b0[subject[5*(i-1) + j]] + b1[subject[5*(i-1) + j]] * post[j] + beta1*treatment[5*(i-1) + j] + beta2*post[j] + beta3*treatment[5*(i-1) + j]*post[j] + beta.age* (age[5*(i-1) + j] - age.bar) # seizure[5*(i-1) + j] ~ dpois(mu[subject[5*(i-1) + j], j]) } } # for (i in 1:N){ b0[i] ~ dnorm(0.0, tau.b0) # subject random effects b1[i] ~ dnorm(0.0, tau.b1) # subject random effects } age.bar <- mean(age[]) # priors: a0 ~ dnorm(0.0,1.0E-4) beta1 ~ dnorm(0.0,1.0E-4) beta2 ~ dnorm(0.0,1.0E-4) beta3 ~ dnorm(0.0,1.0E-4) beta.age ~ dnorm(0.0,1.0E-4) tau.b0 ~ dgamma(1.0E-3,1.0E-3) tau.b1 ~ dgamma(1.0E-3,1.0E-3) sigma2.b0 <- 1.0/ tau.b0 sigma2.b1 <- 1.0/ tau.b1 beta0<- a0 - age.bar*beta.age } # Initial values list(tau.b0 =1, tau.b1=1, a0=0, beta1=0, beta2=0, beta3=0, beta.age=0) list(tau.b0 =3, tau.b1=2, a0=1, beta1=1, beta2=-2, beta3=-2, beta.age=0) # Data list(N=59, t=c(8,2,2,2,2), post=c(0,1,1,1,1)) no[] subject[] treatment[] visit[] seizure[] age[] 1 1 0 0 11 31 2 1 0 1 5 31 3 1 0 2 3 31 4 1 0 3 3 31 5 1 0 4 3 31 6 2 0 0 11 30 7 2 0 1 3 30 8 2 0 2 5 30 9 2 0 3 3 30 10 2 0 4 3 30 11 3 0 0 6 25 12 3 0 1 2 25 13 3 0 2 4 25 14 3 0 3 0 25 15 3 0 4 5 25 16 4 0 0 8 36 17 4 0 1 4 36 18 4 0 2 4 36 19 4 0 3 1 36 20 4 0 4 4 36 21 5 0 0 66 22 22 5 0 1 7 22 23 5 0 2 18 22 24 5 0 3 9 22 25 5 0 4 21 22 26 6 0 0 27 29 27 6 0 1 5 29 28 6 0 2 2 29 29 6 0 3 8 29 30 6 0 4 7 29 31 7 0 0 12 31 32 7 0 1 6 31 33 7 0 2 4 31 34 7 0 3 0 31 35 7 0 4 2 31 36 8 0 0 52 42 37 8 0 1 40 42 38 8 0 2 20 42 39 8 0 3 23 42 40 8 0 4 12 42 41 9 0 0 23 37 42 9 0 1 5 37 43 9 0 2 6 37 44 9 0 3 6 37 45 9 0 4 5 37 46 10 0 0 10 28 47 10 0 1 14 28 48 10 0 2 13 28 49 10 0 3 6 28 50 10 0 4 0 28 51 11 0 0 52 36 52 11 0 1 26 36 53 11 0 2 12 36 54 11 0 3 6 36 55 11 0 4 22 36 56 12 0 0 33 24 57 12 0 1 12 24 58 12 0 2 6 24 59 12 0 3 8 24 60 12 0 4 4 24 61 13 0 0 18 23 62 13 0 1 4 23 63 13 0 2 4 23 64 13 0 3 6 23 65 13 0 4 2 23 66 14 0 0 42 36 67 14 0 1 7 36 68 14 0 2 9 36 69 14 0 3 12 36 70 14 0 4 14 36 71 15 0 0 87 26 72 15 0 1 16 26 73 15 0 2 24 26 74 15 0 3 10 26 75 15 0 4 9 26 76 16 0 0 50 26 77 16 0 1 11 26 78 16 0 2 0 26 79 16 0 3 0 26 80 16 0 4 5 26 81 17 0 0 18 28 82 17 0 1 0 28 83 17 0 2 0 28 84 17 0 3 3 28 85 17 0 4 3 28 86 18 0 0 111 31 87 18 0 1 37 31 88 18 0 2 29 31 89 18 0 3 28 31 90 18 0 4 29 31 91 19 0 0 18 32 92 19 0 1 3 32 93 19 0 2 5 32 94 19 0 3 2 32 95 19 0 4 5 32 96 20 0 0 20 21 97 20 0 1 3 21 98 20 0 2 0 21 99 20 0 3 6 21 100 20 0 4 7 21 101 21 0 0 12 29 102 21 0 1 3 29 103 21 0 2 4 29 104 21 0 3 3 29 105 21 0 4 4 29 106 22 0 0 9 21 107 22 0 1 3 21 108 22 0 2 4 21 109 22 0 3 3 21 110 22 0 4 4 21 111 23 0 0 17 32 112 23 0 1 2 32 113 23 0 2 3 32 114 23 0 3 3 32 115 23 0 4 5 32 116 24 0 0 28 25 117 24 0 1 8 25 118 24 0 2 12 25 119 24 0 3 2 25 120 24 0 4 8 25 121 25 0 0 55 30 122 25 0 1 18 30 123 25 0 2 24 30 124 25 0 3 76 30 125 25 0 4 25 30 126 26 0 0 9 40 127 26 0 1 2 40 128 26 0 2 1 40 129 26 0 3 2 40 130 26 0 4 1 40 131 27 0 0 10 19 132 27 0 1 3 19 133 27 0 2 1 19 134 27 0 3 4 19 135 27 0 4 2 19 136 28 0 0 47 22 137 28 0 1 13 22 138 28 0 2 15 22 139 28 0 3 13 22 140 28 0 4 12 22 141 29 1 0 76 18 142 29 1 1 11 18 143 29 1 2 14 18 144 29 1 3 9 18 145 29 1 4 8 18 146 30 1 0 38 32 147 30 1 1 8 32 148 30 1 2 7 32 149 30 1 3 9 32 150 30 1 4 4 32 151 31 1 0 19 20 152 31 1 1 0 20 153 31 1 2 4 20 154 31 1 3 3 20 155 31 1 4 0 20 156 32 1 0 10 30 157 32 1 1 3 30 158 32 1 2 6 30 159 32 1 3 1 30 160 32 1 4 3 30 161 33 1 0 19 18 162 33 1 1 2 18 163 33 1 2 6 18 164 33 1 3 7 18 165 33 1 4 4 18 166 34 1 0 24 24 167 34 1 1 4 24 168 34 1 2 3 24 169 34 1 3 1 24 170 34 1 4 3 24 171 35 1 0 31 30 172 35 1 1 22 30 173 35 1 2 17 30 174 35 1 3 19 30 175 35 1 4 16 30 176 36 1 0 14 35 177 36 1 1 5 35 178 36 1 2 4 35 179 36 1 3 7 35 180 36 1 4 4 35 181 37 1 0 11 27 182 37 1 1 2 27 183 37 1 2 4 27 184 37 1 3 0 27 185 37 1 4 4 27 186 38 1 0 67 20 187 38 1 1 3 20 188 38 1 2 7 20 189 38 1 3 7 20 190 38 1 4 7 20 191 39 1 0 41 22 192 39 1 1 4 22 193 39 1 2 18 22 194 39 1 3 2 22 195 39 1 4 5 22 196 40 1 0 7 28 197 40 1 1 2 28 198 40 1 2 1 28 199 40 1 3 1 28 200 40 1 4 0 28 201 41 1 0 22 23 202 41 1 1 0 23 203 41 1 2 2 23 204 41 1 3 4 23 205 41 1 4 0 23 206 42 1 0 13 40 207 42 1 1 5 40 208 42 1 2 4 40 209 42 1 3 0 40 210 42 1 4 3 40 211 43 1 0 46 33 212 43 1 1 11 33 213 43 1 2 14 33 214 43 1 3 25 33 215 43 1 4 15 33 216 44 1 0 36 21 217 44 1 1 10 21 218 44 1 2 5 21 219 44 1 3 3 21 220 44 1 4 8 21 221 45 1 0 38 35 222 45 1 1 19 35 223 45 1 2 7 35 224 45 1 3 6 35 225 45 1 4 7 35 226 46 1 0 7 25 227 46 1 1 1 25 228 46 1 2 1 25 229 46 1 3 2 25 230 46 1 4 3 25 231 47 1 0 36 26 232 47 1 1 6 26 233 47 1 2 10 26 234 47 1 3 8 26 235 47 1 4 8 26 236 48 1 0 11 25 237 48 1 1 2 25 238 48 1 2 1 25 239 48 1 3 0 25 240 48 1 4 0 25 241 49 1 0 151 22 242 49 1 1 102 22 243 49 1 2 65 22 244 49 1 3 72 22 245 49 1 4 63 22 246 50 1 0 22 32 247 50 1 1 4 32 248 50 1 2 3 32 249 50 1 3 2 32 250 50 1 4 4 32 251 51 1 0 41 25 252 51 1 1 8 25 253 51 1 2 6 25 254 51 1 3 5 25 255 51 1 4 7 25 256 52 1 0 32 35 257 52 1 1 1 35 258 52 1 2 3 35 259 52 1 3 1 35 260 52 1 4 5 35 261 53 1 0 56 21 262 53 1 1 18 21 263 53 1 2 11 21 264 53 1 3 28 21 265 53 1 4 13 21 266 54 1 0 24 41 267 54 1 1 6 41 268 54 1 2 3 41 269 54 1 3 4 41 270 54 1 4 0 41 271 55 1 0 16 32 272 55 1 1 3 32 273 55 1 2 5 32 274 55 1 3 4 32 275 55 1 4 3 32 276 56 1 0 22 26 277 56 1 1 1 26 278 56 1 2 23 26 279 56 1 3 19 26 280 56 1 4 8 26 281 57 1 0 25 21 282 57 1 1 2 21 283 57 1 2 3 21 284 57 1 3 0 21 285 57 1 4 1 21 286 58 1 0 13 36 287 58 1 1 0 36 288 58 1 2 0 36 289 58 1 3 0 36 290 58 1 4 0 36 291 59 1 0 12 37 292 59 1 1 1 37 293 59 1 2 4 37 294 59 1 3 3 37 295 59 1 4 2 37 END