Before fitting any model in mlumr, you need to:
set_ipd()set_agd()combine_data()add_integration()IPD should be a data frame with one row per patient, containing:
family)library(mlumr)
set.seed(2026)
# Example: Trial A data (index treatment)
trial_a <- data.frame(
patient_id = 1:500,
treatment = "Drug_A",
response = rbinom(500, 1, 0.6),
age_group = rbinom(500, 1, 0.4), # 0 = young, 1 = old
sex = rbinom(500, 1, 0.55) # 0 = male, 1 = female
)
ipd <- set_ipd(
data = trial_a,
treatment = "treatment",
outcome = "response",
covariates = c("age_group", "sex")
)
ipd
#> $data
#> .study .trt .outcome age_group sex
#> 1 IPD_Study Drug_A 0 0 0
#> 2 IPD_Study Drug_A 1 1 0
#> 3 IPD_Study Drug_A 1 1 0
#> 4 IPD_Study Drug_A 1 0 1
#> 5 IPD_Study Drug_A 1 0 1
#> 6 IPD_Study Drug_A 1 0 0
#> 7 IPD_Study Drug_A 1 0 0
#> 8 IPD_Study Drug_A 0 1 1
#> 9 IPD_Study Drug_A 1 1 1
#> 10 IPD_Study Drug_A 1 0 1
#> 11 IPD_Study Drug_A 1 0 1
#> 12 IPD_Study Drug_A 0 0 0
#> 13 IPD_Study Drug_A 1 0 0
#> 14 IPD_Study Drug_A 0 0 1
#> 15 IPD_Study Drug_A 1 1 1
#> 16 IPD_Study Drug_A 1 0 0
#> 17 IPD_Study Drug_A 1 0 1
#> 18 IPD_Study Drug_A 1 1 0
#> 19 IPD_Study Drug_A 1 0 0
#> 20 IPD_Study Drug_A 1 0 1
#> 21 IPD_Study Drug_A 1 1 0
#> 22 IPD_Study Drug_A 1 1 1
#> 23 IPD_Study Drug_A 1 1 0
#> 24 IPD_Study Drug_A 1 1 0
#> 25 IPD_Study Drug_A 1 0 1
#> 26 IPD_Study Drug_A 1 1 0
#> 27 IPD_Study Drug_A 1 0 1
#> 28 IPD_Study Drug_A 1 0 0
#> 29 IPD_Study Drug_A 1 0 0
#> 30 IPD_Study Drug_A 0 0 0
#> 31 IPD_Study Drug_A 0 0 1
#> 32 IPD_Study Drug_A 1 0 1
#> 33 IPD_Study Drug_A 0 0 1
#> 34 IPD_Study Drug_A 1 0 1
#> 35 IPD_Study Drug_A 1 1 0
#> 36 IPD_Study Drug_A 1 0 0
#> 37 IPD_Study Drug_A 1 1 1
#> 38 IPD_Study Drug_A 1 0 1
#> 39 IPD_Study Drug_A 0 0 1
#> 40 IPD_Study Drug_A 1 0 1
#> 41 IPD_Study Drug_A 1 1 1
#> 42 IPD_Study Drug_A 0 1 1
#> 43 IPD_Study Drug_A 1 0 0
#> 44 IPD_Study Drug_A 1 1 1
#> 45 IPD_Study Drug_A 1 0 1
#> 46 IPD_Study Drug_A 1 0 0
#> 47 IPD_Study Drug_A 0 1 1
#> 48 IPD_Study Drug_A 1 0 1
#> 49 IPD_Study Drug_A 1 1 1
#> 50 IPD_Study Drug_A 1 0 0
#> 51 IPD_Study Drug_A 0 1 0
#> 52 IPD_Study Drug_A 0 0 0
#> 53 IPD_Study Drug_A 0 1 1
#> 54 IPD_Study Drug_A 0 0 1
#> 55 IPD_Study Drug_A 1 0 0
#> 56 IPD_Study Drug_A 0 0 1
#> 57 IPD_Study Drug_A 0 0 1
#> 58 IPD_Study Drug_A 1 1 1
#> 59 IPD_Study Drug_A 0 0 0
#> 60 IPD_Study Drug_A 1 0 0
#> 61 IPD_Study Drug_A 1 1 0
#> 62 IPD_Study Drug_A 0 1 1
#> 63 IPD_Study Drug_A 1 1 1
#> 64 IPD_Study Drug_A 1 0 1
#> 65 IPD_Study Drug_A 1 0 0
#> 66 IPD_Study Drug_A 1 0 1
#> 67 IPD_Study Drug_A 0 0 1
#> 68 IPD_Study Drug_A 1 0 1
#> 69 IPD_Study Drug_A 1 1 0
#> 70 IPD_Study Drug_A 0 0 1
#> 71 IPD_Study Drug_A 0 0 0
#> 72 IPD_Study Drug_A 1 1 0
#> 73 IPD_Study Drug_A 0 0 0
#> 74 IPD_Study Drug_A 0 0 0
#> 75 IPD_Study Drug_A 1 0 0
#> 76 IPD_Study Drug_A 0 0 1
#> 77 IPD_Study Drug_A 0 1 1
#> 78 IPD_Study Drug_A 1 0 1
#> 79 IPD_Study Drug_A 1 0 0
#> 80 IPD_Study Drug_A 0 1 0
#> 81 IPD_Study Drug_A 1 1 0
#> 82 IPD_Study Drug_A 0 1 0
#> 83 IPD_Study Drug_A 0 0 0
#> 84 IPD_Study Drug_A 0 0 1
#> 85 IPD_Study Drug_A 1 1 0
#> 86 IPD_Study Drug_A 0 0 0
#> 87 IPD_Study Drug_A 0 1 0
#> 88 IPD_Study Drug_A 0 1 0
#> 89 IPD_Study Drug_A 1 0 1
#> 90 IPD_Study Drug_A 1 0 0
#> 91 IPD_Study Drug_A 0 1 1
#> 92 IPD_Study Drug_A 0 0 0
#> 93 IPD_Study Drug_A 0 0 1
#> 94 IPD_Study Drug_A 1 0 1
#> 95 IPD_Study Drug_A 1 0 1
#> 96 IPD_Study Drug_A 1 0 1
#> 97 IPD_Study Drug_A 0 0 0
#> 98 IPD_Study Drug_A 1 1 1
#> 99 IPD_Study Drug_A 0 0 1
#> 100 IPD_Study Drug_A 1 1 0
#> 101 IPD_Study Drug_A 1 0 0
#> 102 IPD_Study Drug_A 0 1 1
#> 103 IPD_Study Drug_A 1 0 0
#> 104 IPD_Study Drug_A 1 1 1
#> 105 IPD_Study Drug_A 1 0 0
#> 106 IPD_Study Drug_A 1 0 1
#> 107 IPD_Study Drug_A 0 1 1
#> 108 IPD_Study Drug_A 0 1 0
#> 109 IPD_Study Drug_A 1 0 1
#> 110 IPD_Study Drug_A 1 1 1
#> 111 IPD_Study Drug_A 1 0 0
#> 112 IPD_Study Drug_A 0 0 0
#> 113 IPD_Study Drug_A 1 0 1
#> 114 IPD_Study Drug_A 1 1 0
#> 115 IPD_Study Drug_A 1 1 1
#> 116 IPD_Study Drug_A 1 1 0
#> 117 IPD_Study Drug_A 1 1 1
#> 118 IPD_Study Drug_A 1 0 1
#> 119 IPD_Study Drug_A 1 1 0
#> 120 IPD_Study Drug_A 0 0 1
#> 121 IPD_Study Drug_A 1 0 1
#> 122 IPD_Study Drug_A 1 0 1
#> 123 IPD_Study Drug_A 1 0 0
#> 124 IPD_Study Drug_A 0 1 0
#> 125 IPD_Study Drug_A 1 1 1
#> 126 IPD_Study Drug_A 1 0 0
#> 127 IPD_Study Drug_A 1 1 1
#> 128 IPD_Study Drug_A 1 1 0
#> 129 IPD_Study Drug_A 1 0 1
#> 130 IPD_Study Drug_A 1 1 0
#> 131 IPD_Study Drug_A 1 0 0
#> 132 IPD_Study Drug_A 0 0 0
#> 133 IPD_Study Drug_A 1 0 1
#> 134 IPD_Study Drug_A 1 1 1
#> 135 IPD_Study Drug_A 0 0 0
#> 136 IPD_Study Drug_A 1 1 1
#> 137 IPD_Study Drug_A 1 1 1
#> 138 IPD_Study Drug_A 1 1 1
#> 139 IPD_Study Drug_A 1 1 0
#> 140 IPD_Study Drug_A 1 0 1
#> 141 IPD_Study Drug_A 0 1 0
#> 142 IPD_Study Drug_A 1 1 0
#> 143 IPD_Study Drug_A 1 0 1
#> 144 IPD_Study Drug_A 0 0 0
#> 145 IPD_Study Drug_A 0 0 0
#> 146 IPD_Study Drug_A 1 1 0
#> 147 IPD_Study Drug_A 1 1 0
#> 148 IPD_Study Drug_A 0 0 0
#> 149 IPD_Study Drug_A 0 1 0
#> 150 IPD_Study Drug_A 1 0 1
#> 151 IPD_Study Drug_A 1 1 0
#> 152 IPD_Study Drug_A 1 1 1
#> 153 IPD_Study Drug_A 0 1 0
#> 154 IPD_Study Drug_A 1 0 1
#> 155 IPD_Study Drug_A 0 0 0
#> 156 IPD_Study Drug_A 0 0 0
#> 157 IPD_Study Drug_A 1 1 0
#> 158 IPD_Study Drug_A 0 0 1
#> 159 IPD_Study Drug_A 0 1 0
#> 160 IPD_Study Drug_A 1 1 1
#> 161 IPD_Study Drug_A 1 0 1
#> 162 IPD_Study Drug_A 1 0 0
#> 163 IPD_Study Drug_A 0 0 0
#> 164 IPD_Study Drug_A 1 0 0
#> 165 IPD_Study Drug_A 1 0 0
#> 166 IPD_Study Drug_A 1 0 0
#> 167 IPD_Study Drug_A 1 0 0
#> 168 IPD_Study Drug_A 1 1 1
#> 169 IPD_Study Drug_A 1 0 0
#> 170 IPD_Study Drug_A 1 1 0
#> 171 IPD_Study Drug_A 0 1 0
#> 172 IPD_Study Drug_A 0 1 0
#> 173 IPD_Study Drug_A 1 0 1
#> 174 IPD_Study Drug_A 0 0 0
#> 175 IPD_Study Drug_A 1 0 0
#> 176 IPD_Study Drug_A 1 1 1
#> 177 IPD_Study Drug_A 0 1 1
#> 178 IPD_Study Drug_A 0 1 1
#> 179 IPD_Study Drug_A 0 1 1
#> 180 IPD_Study Drug_A 1 1 1
#> 181 IPD_Study Drug_A 1 1 1
#> 182 IPD_Study Drug_A 1 0 0
#> 183 IPD_Study Drug_A 0 1 1
#> 184 IPD_Study Drug_A 0 0 1
#> 185 IPD_Study Drug_A 1 1 0
#> 186 IPD_Study Drug_A 1 0 1
#> 187 IPD_Study Drug_A 0 0 0
#> 188 IPD_Study Drug_A 1 0 1
#> 189 IPD_Study Drug_A 1 0 1
#> 190 IPD_Study Drug_A 1 0 1
#> 191 IPD_Study Drug_A 1 1 1
#> 192 IPD_Study Drug_A 1 0 0
#> 193 IPD_Study Drug_A 1 1 1
#> 194 IPD_Study Drug_A 0 0 1
#> 195 IPD_Study Drug_A 1 1 1
#> 196 IPD_Study Drug_A 1 1 1
#> 197 IPD_Study Drug_A 1 0 1
#> 198 IPD_Study Drug_A 1 0 0
#> 199 IPD_Study Drug_A 0 1 1
#> 200 IPD_Study Drug_A 1 0 1
#> 201 IPD_Study Drug_A 0 1 0
#> 202 IPD_Study Drug_A 0 0 1
#> 203 IPD_Study Drug_A 1 0 1
#> 204 IPD_Study Drug_A 0 0 1
#> 205 IPD_Study Drug_A 1 1 1
#> 206 IPD_Study Drug_A 0 0 0
#> 207 IPD_Study Drug_A 1 0 0
#> 208 IPD_Study Drug_A 0 1 0
#> 209 IPD_Study Drug_A 0 0 1
#> 210 IPD_Study Drug_A 1 0 1
#> 211 IPD_Study Drug_A 0 0 1
#> 212 IPD_Study Drug_A 1 1 0
#> 213 IPD_Study Drug_A 1 0 1
#> 214 IPD_Study Drug_A 1 0 1
#> 215 IPD_Study Drug_A 0 0 1
#> 216 IPD_Study Drug_A 1 1 1
#> 217 IPD_Study Drug_A 1 0 1
#> 218 IPD_Study Drug_A 1 0 0
#> 219 IPD_Study Drug_A 1 0 0
#> 220 IPD_Study Drug_A 1 1 1
#> 221 IPD_Study Drug_A 1 1 0
#> 222 IPD_Study Drug_A 0 0 1
#> 223 IPD_Study Drug_A 1 0 1
#> 224 IPD_Study Drug_A 0 0 1
#> 225 IPD_Study Drug_A 1 0 0
#> 226 IPD_Study Drug_A 0 1 1
#> 227 IPD_Study Drug_A 0 0 1
#> 228 IPD_Study Drug_A 1 0 0
#> 229 IPD_Study Drug_A 1 1 1
#> 230 IPD_Study Drug_A 1 0 1
#> 231 IPD_Study Drug_A 1 1 1
#> 232 IPD_Study Drug_A 1 0 0
#> 233 IPD_Study Drug_A 1 0 1
#> 234 IPD_Study Drug_A 1 0 0
#> 235 IPD_Study Drug_A 0 0 1
#> 236 IPD_Study Drug_A 0 1 1
#> 237 IPD_Study Drug_A 0 1 0
#> 238 IPD_Study Drug_A 1 0 0
#> 239 IPD_Study Drug_A 1 1 0
#> 240 IPD_Study Drug_A 0 1 1
#> 241 IPD_Study Drug_A 0 1 0
#> 242 IPD_Study Drug_A 1 0 0
#> 243 IPD_Study Drug_A 0 0 1
#> 244 IPD_Study Drug_A 0 0 1
#> 245 IPD_Study Drug_A 1 1 1
#> 246 IPD_Study Drug_A 1 0 1
#> 247 IPD_Study Drug_A 0 0 0
#> 248 IPD_Study Drug_A 1 1 1
#> 249 IPD_Study Drug_A 1 1 0
#> 250 IPD_Study Drug_A 1 1 1
#> 251 IPD_Study Drug_A 0 0 0
#> 252 IPD_Study Drug_A 1 0 1
#> 253 IPD_Study Drug_A 1 1 1
#> 254 IPD_Study Drug_A 0 0 0
#> 255 IPD_Study Drug_A 1 0 0
#> 256 IPD_Study Drug_A 1 0 1
#> 257 IPD_Study Drug_A 0 0 0
#> 258 IPD_Study Drug_A 0 0 1
#> 259 IPD_Study Drug_A 0 1 1
#> 260 IPD_Study Drug_A 0 0 0
#> 261 IPD_Study Drug_A 1 0 1
#> 262 IPD_Study Drug_A 1 0 0
#> 263 IPD_Study Drug_A 0 1 1
#> 264 IPD_Study Drug_A 1 1 1
#> 265 IPD_Study Drug_A 0 0 0
#> 266 IPD_Study Drug_A 1 0 0
#> 267 IPD_Study Drug_A 1 1 0
#> 268 IPD_Study Drug_A 1 1 0
#> 269 IPD_Study Drug_A 0 0 1
#> 270 IPD_Study Drug_A 0 0 1
#> 271 IPD_Study Drug_A 1 0 1
#> 272 IPD_Study Drug_A 1 1 0
#> 273 IPD_Study Drug_A 1 1 1
#> 274 IPD_Study Drug_A 1 0 1
#> 275 IPD_Study Drug_A 0 0 0
#> 276 IPD_Study Drug_A 1 0 0
#> 277 IPD_Study Drug_A 0 0 1
#> 278 IPD_Study Drug_A 0 1 1
#> 279 IPD_Study Drug_A 0 1 1
#> 280 IPD_Study Drug_A 1 1 1
#> 281 IPD_Study Drug_A 0 0 0
#> 282 IPD_Study Drug_A 0 1 0
#> 283 IPD_Study Drug_A 1 1 1
#> 284 IPD_Study Drug_A 0 0 1
#> 285 IPD_Study Drug_A 0 0 0
#> 286 IPD_Study Drug_A 1 1 0
#> 287 IPD_Study Drug_A 1 1 1
#> 288 IPD_Study Drug_A 1 0 0
#> 289 IPD_Study Drug_A 0 0 1
#> 290 IPD_Study Drug_A 0 0 0
#> 291 IPD_Study Drug_A 1 0 1
#> 292 IPD_Study Drug_A 0 0 0
#> 293 IPD_Study Drug_A 0 1 1
#> 294 IPD_Study Drug_A 0 0 1
#> 295 IPD_Study Drug_A 0 0 1
#> 296 IPD_Study Drug_A 0 1 0
#> 297 IPD_Study Drug_A 1 1 1
#> 298 IPD_Study Drug_A 1 0 1
#> 299 IPD_Study Drug_A 1 0 1
#> 300 IPD_Study Drug_A 1 0 1
#> 301 IPD_Study Drug_A 1 1 0
#> 302 IPD_Study Drug_A 1 0 1
#> 303 IPD_Study Drug_A 0 1 0
#> 304 IPD_Study Drug_A 1 1 0
#> 305 IPD_Study Drug_A 0 0 1
#> 306 IPD_Study Drug_A 1 1 0
#> 307 IPD_Study Drug_A 0 0 1
#> 308 IPD_Study Drug_A 1 1 1
#> 309 IPD_Study Drug_A 0 0 0
#> 310 IPD_Study Drug_A 0 1 0
#> 311 IPD_Study Drug_A 0 0 0
#> 312 IPD_Study Drug_A 0 1 0
#> 313 IPD_Study Drug_A 1 1 1
#> 314 IPD_Study Drug_A 0 1 1
#> 315 IPD_Study Drug_A 1 1 0
#> 316 IPD_Study Drug_A 0 1 1
#> 317 IPD_Study Drug_A 1 0 0
#> 318 IPD_Study Drug_A 1 0 0
#> 319 IPD_Study Drug_A 0 0 1
#> 320 IPD_Study Drug_A 0 1 0
#> 321 IPD_Study Drug_A 0 1 1
#> 322 IPD_Study Drug_A 1 0 0
#> 323 IPD_Study Drug_A 1 0 1
#> 324 IPD_Study Drug_A 0 0 1
#> 325 IPD_Study Drug_A 1 1 1
#> 326 IPD_Study Drug_A 0 1 0
#> 327 IPD_Study Drug_A 0 1 1
#> 328 IPD_Study Drug_A 0 0 1
#> 329 IPD_Study Drug_A 1 0 1
#> 330 IPD_Study Drug_A 0 0 0
#> 331 IPD_Study Drug_A 1 0 0
#> 332 IPD_Study Drug_A 0 0 1
#> 333 IPD_Study Drug_A 0 1 0
#> 334 IPD_Study Drug_A 0 1 1
#> 335 IPD_Study Drug_A 1 1 1
#> 336 IPD_Study Drug_A 0 1 0
#> 337 IPD_Study Drug_A 1 1 1
#> 338 IPD_Study Drug_A 1 1 0
#> 339 IPD_Study Drug_A 1 0 1
#> 340 IPD_Study Drug_A 0 0 0
#> 341 IPD_Study Drug_A 1 1 1
#> 342 IPD_Study Drug_A 1 1 1
#> 343 IPD_Study Drug_A 1 0 1
#> 344 IPD_Study Drug_A 1 1 1
#> 345 IPD_Study Drug_A 0 1 1
#> 346 IPD_Study Drug_A 1 0 0
#> 347 IPD_Study Drug_A 1 0 1
#> 348 IPD_Study Drug_A 1 1 1
#> 349 IPD_Study Drug_A 1 0 1
#> 350 IPD_Study Drug_A 1 0 1
#> 351 IPD_Study Drug_A 1 1 0
#> 352 IPD_Study Drug_A 0 0 1
#> 353 IPD_Study Drug_A 1 0 1
#> 354 IPD_Study Drug_A 1 0 0
#> 355 IPD_Study Drug_A 0 0 0
#> 356 IPD_Study Drug_A 0 1 1
#> 357 IPD_Study Drug_A 0 1 1
#> 358 IPD_Study Drug_A 1 1 1
#> 359 IPD_Study Drug_A 1 0 0
#> 360 IPD_Study Drug_A 0 0 1
#> 361 IPD_Study Drug_A 1 0 0
#> 362 IPD_Study Drug_A 1 0 1
#> 363 IPD_Study Drug_A 0 1 1
#> 364 IPD_Study Drug_A 1 0 0
#> 365 IPD_Study Drug_A 0 1 1
#> 366 IPD_Study Drug_A 1 0 1
#> 367 IPD_Study Drug_A 0 0 1
#> 368 IPD_Study Drug_A 0 0 1
#> 369 IPD_Study Drug_A 1 1 1
#> 370 IPD_Study Drug_A 0 1 0
#> 371 IPD_Study Drug_A 0 0 0
#> 372 IPD_Study Drug_A 1 1 0
#> 373 IPD_Study Drug_A 0 0 1
#> 374 IPD_Study Drug_A 1 0 1
#> 375 IPD_Study Drug_A 0 0 1
#> 376 IPD_Study Drug_A 1 0 1
#> 377 IPD_Study Drug_A 0 0 1
#> 378 IPD_Study Drug_A 1 0 0
#> 379 IPD_Study Drug_A 1 1 0
#> 380 IPD_Study Drug_A 1 0 1
#> 381 IPD_Study Drug_A 0 1 0
#> 382 IPD_Study Drug_A 0 1 1
#> 383 IPD_Study Drug_A 1 1 1
#> 384 IPD_Study Drug_A 1 0 0
#> 385 IPD_Study Drug_A 0 0 1
#> 386 IPD_Study Drug_A 0 0 1
#> 387 IPD_Study Drug_A 0 0 1
#> 388 IPD_Study Drug_A 1 0 0
#> 389 IPD_Study Drug_A 1 1 0
#> 390 IPD_Study Drug_A 1 0 0
#> 391 IPD_Study Drug_A 0 0 1
#> 392 IPD_Study Drug_A 0 0 0
#> 393 IPD_Study Drug_A 1 1 0
#> 394 IPD_Study Drug_A 1 0 1
#> 395 IPD_Study Drug_A 1 0 0
#> 396 IPD_Study Drug_A 0 1 0
#> 397 IPD_Study Drug_A 1 1 0
#> 398 IPD_Study Drug_A 0 0 0
#> 399 IPD_Study Drug_A 1 0 1
#> 400 IPD_Study Drug_A 1 0 1
#> 401 IPD_Study Drug_A 1 1 0
#> 402 IPD_Study Drug_A 1 1 0
#> 403 IPD_Study Drug_A 0 1 1
#> 404 IPD_Study Drug_A 1 0 1
#> 405 IPD_Study Drug_A 1 1 0
#> 406 IPD_Study Drug_A 1 0 1
#> 407 IPD_Study Drug_A 1 0 0
#> 408 IPD_Study Drug_A 1 1 1
#> 409 IPD_Study Drug_A 1 1 0
#> 410 IPD_Study Drug_A 0 0 1
#> 411 IPD_Study Drug_A 0 1 1
#> 412 IPD_Study Drug_A 1 0 0
#> 413 IPD_Study Drug_A 1 1 0
#> 414 IPD_Study Drug_A 0 0 0
#> 415 IPD_Study Drug_A 1 1 0
#> 416 IPD_Study Drug_A 1 1 0
#> 417 IPD_Study Drug_A 1 1 1
#> 418 IPD_Study Drug_A 1 0 0
#> 419 IPD_Study Drug_A 0 1 1
#> 420 IPD_Study Drug_A 1 0 1
#> 421 IPD_Study Drug_A 1 1 0
#> 422 IPD_Study Drug_A 1 1 1
#> 423 IPD_Study Drug_A 0 1 0
#> 424 IPD_Study Drug_A 1 1 1
#> 425 IPD_Study Drug_A 1 1 0
#> 426 IPD_Study Drug_A 1 0 1
#> 427 IPD_Study Drug_A 1 1 0
#> 428 IPD_Study Drug_A 1 1 0
#> 429 IPD_Study Drug_A 1 1 1
#> 430 IPD_Study Drug_A 0 0 0
#> 431 IPD_Study Drug_A 0 1 1
#> 432 IPD_Study Drug_A 1 0 0
#> 433 IPD_Study Drug_A 0 0 1
#> 434 IPD_Study Drug_A 1 0 0
#> 435 IPD_Study Drug_A 0 0 0
#> 436 IPD_Study Drug_A 1 1 0
#> 437 IPD_Study Drug_A 0 1 1
#> 438 IPD_Study Drug_A 1 1 1
#> 439 IPD_Study Drug_A 0 1 0
#> 440 IPD_Study Drug_A 1 0 0
#> 441 IPD_Study Drug_A 1 0 0
#> 442 IPD_Study Drug_A 1 1 0
#> 443 IPD_Study Drug_A 1 0 1
#> 444 IPD_Study Drug_A 1 1 1
#> 445 IPD_Study Drug_A 1 0 0
#> 446 IPD_Study Drug_A 0 1 0
#> 447 IPD_Study Drug_A 1 0 0
#> 448 IPD_Study Drug_A 1 0 1
#> 449 IPD_Study Drug_A 1 0 0
#> 450 IPD_Study Drug_A 1 0 1
#> 451 IPD_Study Drug_A 0 1 1
#> 452 IPD_Study Drug_A 1 0 1
#> 453 IPD_Study Drug_A 1 0 1
#> 454 IPD_Study Drug_A 0 0 0
#> 455 IPD_Study Drug_A 1 1 1
#> 456 IPD_Study Drug_A 1 0 1
#> 457 IPD_Study Drug_A 0 0 0
#> 458 IPD_Study Drug_A 0 1 0
#> 459 IPD_Study Drug_A 0 0 1
#> 460 IPD_Study Drug_A 0 0 0
#> 461 IPD_Study Drug_A 1 1 1
#> 462 IPD_Study Drug_A 0 1 1
#> 463 IPD_Study Drug_A 0 1 0
#> 464 IPD_Study Drug_A 1 0 1
#> 465 IPD_Study Drug_A 0 0 0
#> 466 IPD_Study Drug_A 1 1 1
#> 467 IPD_Study Drug_A 0 0 0
#> 468 IPD_Study Drug_A 1 0 1
#> 469 IPD_Study Drug_A 1 1 0
#> 470 IPD_Study Drug_A 1 0 0
#> 471 IPD_Study Drug_A 0 1 1
#> 472 IPD_Study Drug_A 1 1 1
#> 473 IPD_Study Drug_A 1 1 1
#> 474 IPD_Study Drug_A 1 1 1
#> 475 IPD_Study Drug_A 0 1 0
#> 476 IPD_Study Drug_A 0 0 1
#> 477 IPD_Study Drug_A 0 0 1
#> 478 IPD_Study Drug_A 1 1 1
#> 479 IPD_Study Drug_A 0 0 0
#> 480 IPD_Study Drug_A 0 0 1
#> 481 IPD_Study Drug_A 0 1 0
#> 482 IPD_Study Drug_A 1 0 1
#> 483 IPD_Study Drug_A 1 1 0
#> 484 IPD_Study Drug_A 1 0 0
#> 485 IPD_Study Drug_A 0 0 1
#> 486 IPD_Study Drug_A 0 0 0
#> 487 IPD_Study Drug_A 0 1 1
#> 488 IPD_Study Drug_A 1 1 0
#> 489 IPD_Study Drug_A 1 0 0
#> 490 IPD_Study Drug_A 0 0 1
#> 491 IPD_Study Drug_A 1 0 1
#> 492 IPD_Study Drug_A 0 0 1
#> 493 IPD_Study Drug_A 1 1 1
#> 494 IPD_Study Drug_A 0 0 1
#> 495 IPD_Study Drug_A 1 0 1
#> 496 IPD_Study Drug_A 1 0 0
#> 497 IPD_Study Drug_A 0 0 1
#> 498 IPD_Study Drug_A 0 1 0
#> 499 IPD_Study Drug_A 1 0 0
#> 500 IPD_Study Drug_A 0 0 0
#>
#> $n
#> [1] 500
#>
#> $treatment
#> [1] "Drug_A"
#>
#> $covariates
#> [1] "age_group" "sex"
#>
#> $family
#> [1] "binomial"
#>
#> $type
#> [1] "ipd"
#>
#> $n_events
#> [1] 303
#>
#> attr(,"class")
#> [1] "mlumr_ipd" "list"For continuous outcomes, use family = "normal". The IPD
frame needs a numeric outcome column:
trial_a_normal <- data.frame(
patient_id = 1:500,
treatment = "Drug_A",
score = rnorm(500, mean = 3.0, sd = 1.2),
age_group = rbinom(500, 1, 0.4),
sex = rbinom(500, 1, 0.55)
)
ipd_normal <- set_ipd(
data = trial_a_normal,
treatment = "treatment",
outcome = "score",
covariates = c("age_group", "sex"),
family = "normal"
)For Poisson count data, use family = "poisson" and
supply an exposure column:
trial_a_poisson <- data.frame(
patient_id = 1:500,
treatment = "Drug_A",
events = rpois(500, lambda = 0.8),
person_years = runif(500, 0.5, 2.0),
age_group = rbinom(500, 1, 0.4),
sex = rbinom(500, 1, 0.55)
)
ipd_poisson <- set_ipd(
data = trial_a_poisson,
treatment = "treatment",
outcome = "events",
covariates = c("age_group", "sex"),
family = "poisson",
exposure = "person_years"
)set_ipd() checks that:
AgD should be a data frame with one row per study/subgroup, containing:
family):
outcome_n) and
number of events (outcome_r)outcome_mean)
and standard error (outcome_se), optionally sample size
(outcome_n)outcome_r)
and total exposure (outcome_E)# Example: Trial B data (comparator treatment)
trial_b <- data.frame(
study = "Trial_B",
treatment = "Drug_B",
n_total = 400,
n_events = 160,
age_group_mean = 0.35, # proportion in "old" group
sex_prop = 0.50 # proportion female
)
agd <- set_agd(
data = trial_b,
treatment = "treatment",
outcome_n = "n_total",
outcome_r = "n_events",
cov_means = c("age_group_mean", "sex_prop"),
cov_types = c("binary", "binary")
)agd_normal <- set_agd(
data = data.frame(
trt = "Drug_B", y_mean = 3.2, se = 0.15, n = 400,
age_group_mean = 0.35, sex_prop = 0.50
),
treatment = "trt",
family = "normal",
outcome_mean = "y_mean",
outcome_se = "se",
outcome_n = "n",
cov_means = c("age_group_mean", "sex_prop"),
cov_types = c("binary", "binary")
)Scale requirement for normal outcomes.
set_agd() assumes the AgD mean (outcome_mean)
and SE (outcome_se) are on the arithmetic
(original, untransformed) scale, regardless of whether
mlumr() is later called with link = "identity"
or link = "log". If a publication reports only a log-scale
mean and SD, or a geometric mean, back-transform before calling
set_agd() and propagate uncertainty via the delta method;
passing log-scale summaries silently biases the posterior because the
Stan likelihood is normal(E[exp(eta)], se_agd) under the
log link, expecting the AgD quantities already on the outcome scale.
agd_poisson <- set_agd(
data = data.frame(
trt = "Drug_B", n_events = 120, person_years = 800,
age_group_mean = 0.35, sex_prop = 0.50
),
treatment = "trt",
family = "poisson",
outcome_r = "n_events",
outcome_E = "person_years",
cov_means = c("age_group_mean", "sex_prop"),
cov_types = c("binary", "binary")
)set_agd() automatically strips _mean and
_prop suffixes from covariate names to match the IPD
covariate names:
"age_group_mean" -> covariate name
"age_group""sex_prop" -> covariate name "sex"Make sure the resulting names match those in your IPD.
For continuous covariates, also supply standard deviations:
dat <- combine_data(ipd, agd)
print(dat)
#> Unanchored Comparison Data (Binary)
#> ====================================
#>
#> Index treatment (IPD): Drug_A
#> N = 500
#> Events = 303 (60.6%)
#>
#> Comparator treatment (AgD): Drug_B
#> N = 400
#> Events = 160 (40.0%)
#>
#> Covariates ( 2 ): age_group, sex
#> Integration points: not yet added (use add_integration())Integration points are needed for ML-UMR’s numerical integration over the comparator population’s covariate distribution. They are generated using Sobol quasi-Monte Carlo sequences with a Gaussian copula to respect covariate correlations.
dat <- add_integration(
dat,
n_int = 64,
age_group = distr(qbern, prob = age_group_mean),
sex = distr(qbern, prob = sex_mean)
)Key points:
n_int: Number of integration points
(powers of 2 recommended: 32, 64, 128, 256). More points = better
accuracy but slower Stan fitting.distr(): Wraps a quantile function
with parameters. Parameters can reference columns in the AgD data (e.g.,
age_group_mean).qbern: Bernoulli quantile function
(provided by mlumr) for binary covariates. Use qnorm for
continuous covariates.By default, correlations between covariates are estimated from the
IPD using Spearman rank correlation, then adjusted for the Gaussian
copula. For binary and mixed discrete/continuous covariate pairs, this
adjustment is a pragmatic approximation — exact calibration of the
latent Gaussian correlation depends on marginal prevalences, which are
not used in the current transform. For most practical datasets this
provides adequate integration-point distributions, but users with strong
prior information on covariate correlations can supply their own matrix
via the cor argument.
# Default: Spearman correlation from IPD
dat <- add_integration(dat, n_int = 64,
age_group = distr(qbern, prob = age_group_mean),
sex = distr(qbern, prob = sex_mean))
# Supply your own correlation matrix
my_cor <- matrix(c(1, 0.3, 0.3, 1), 2, 2)
dat <- add_integration(dat, n_int = 64, cor = my_cor,
age_group = distr(qbern, prob = age_group_mean),
sex = distr(qbern, prob = sex_mean))
# No correlation adjustment
dat <- add_integration(dat, n_int = 64, cor_adjust = "none",
age_group = distr(qbern, prob = age_group_mean),
sex = distr(qbern, prob = sex_mean))# Expand to long format
int_df <- unnest_integration(dat)
head(int_df)
#> age_group sex .int_id .agd_row
#> 1 0 0 1 1
#> 2 1 0 2 1
#> 3 0 1 3 1
#> 4 0 0 4 1
#> 5 1 1 5 1
#> 6 0 0 6 1Use check_integration() to compare the generated
integration points against the requested AgD marginal distributions and
correlation structure:
check_integration(
dat,
age_group = distr(qbern, prob = age_group_mean),
sex = distr(qbern, prob = sex_mean)
)
#> Integration check: n_int = 64 vs 128
#> Marginals -- max relative difference: 0.0159
#> CAUTION: 1-5%% marginal relative difference. Consider increasing n_int.
#> Joint -- max |cor(current) - cor(doubled)|: 0.0113
#> OK joint: pairwise correlations agree within 0.05.This is especially important when there are several covariates, strong correlations, binary covariates with prevalences near 0 or 1, or a small number of integration points.
The STC and naive methods do not require integration
points. The STC uses integration points if available (for better
marginalization), but falls back to AgD covariate means if they are not.
You can call stc() and naive() immediately
after combine_data().