R生存分析AFT
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1. Surv
Description
创建一个生存对象，通常用作模型公式中的响应变量。 参数匹配是此功能的特殊功能，请参阅下面的详细信息。
Surv(time, time2, event,
type=c(‘right‘, ‘left‘, ‘interval‘, ‘counting‘, ‘interval2‘, ‘mstate‘),
origin=0)
is.Surv(x)
Arguments
time
对于右删失数据，这是一个跟踪时间。对于区间数据，第一个参数是区间的开始时间。
event
状态指示，通常，0=活着，1=死亡。其他选择是TRUE/FALSE (TRUE = 死亡) or 1/2 (2=死亡)。对于区间删失数据，状态指示，0=右删失, 1=事件时间, 2=左删失, 3=区间删失.
右删失(Right Censoring)：只知道实际寿命大于某数；
左删失(Left Censoring)：只知道实际寿命小于某数；
区间删失(Interval Censoring)：只知道实际寿命在一个时间区间内。
time2
区间删失区间的结束时间或仅对过程数据进行计数。
type
指定删失类型。 "right", "left", "counting", "interval", "interval2" or "mstate".
如果event变量是一个因子，假定type="mstate"。如果没有指定参数time2，type="right"；如果指定参数time2，type="counting"
Surv使用示例
> str(lung) ‘data.frame‘: 228 obs. of 10 variables: $ inst : num 3 3 3 5 1 12 7 11 1 7 ... $ time : num 306 455 1010 210 883 ... $ status : num 2 2 1 2 2 1 2 2 2 2 ... $ age : num 74 68 56 57 60 74 68 71 53 61 ... $ sex : num 1 1 1 1 1 1 2 2 1 1 ... $ ph.ecog : num 1 0 0 1 0 1 2 2 1 2 ... $ ph.karno : num 90 90 90 90 100 50 70 60 70 70 ... $ pat.karno: num 100 90 90 60 90 80 60 80 80 70 ... $ meal.cal : num 1175 1225 NA 1150 NA ... $ wt.loss : num NA 15 15 11 0 0 10 1 16 34 ... > with(lung, Surv(time, status)) [1] 306 455 1010+ 210 883 1022+ 310 361 218 [10] 166 170 654 728 71 567 144 613 707 [19] 61 88 301 81 624 371 394 520 574 [28] 118 390 12 473 26 533 107 53 122 [37] 814 965+ 93 731 460 153 433 145 583 [46] 95 303 519 643 765 735 189 53 246 [55] 689 65 5 132 687 345 444 223 175 [64] 60 163 65 208 821+ 428 230 840+ 305 [73] 11 132 226 426 705 363 11 176 791 [82] 95 196+ 167 806+ 284 641 147 740+ 163 [91] 655 239 88 245 588+ 30 179 310 477 [100] 166 559+ 450 364 107 177 156 529+ 11 [109] 429 351 15 181 283 201 524 13 212 [118] 524 288 363 442 199 550 54 558 207 [127] 92 60 551+ 543+ 293 202 353 511+ 267 [136] 511+ 371 387 457 337 201 404+ 222 62 [145] 458+ 356+ 353 163 31 340 229 444+ 315+ [154] 182 156 329 364+ 291 179 376+ 384+ 268 [163] 292+ 142 413+ 266+ 194 320 181 285 301+ [172] 348 197 382+ 303+ 296+ 180 186 145 269+ [181] 300+ 284+ 350 272+ 292+ 332+ 285 259+ 110 [190] 286 270 81 131 225+ 269 225+ 243+ 279+ [199] 276+ 135 79 59 240+ 202+ 235+ 105 224+ [208] 239 237+ 173+ 252+ 221+ 185+ 92+ 13 222+ [217] 192+ 183 211+ 175+ 197+ 203+ 116 188+ 191+ [226] 105+ 174+ 177+ > str(heart) ‘data.frame‘: 172 obs. of 8 variables: $ start : num 0 0 0 1 0 36 0 0 0 51 ... $ stop : num 50 6 1 16 36 39 18 3 51 675 ... $ event : num 1 1 0 1 0 1 1 1 0 1 ... $ age : num -17.16 3.84 6.3 6.3 -7.74 ... $ year : num 0.123 0.255 0.266 0.266 0.49 ... $ surgery : num 0 0 0 0 0 0 0 0 0 0 ... $ transplant: Factor w/ 2 levels "0","1": 1 1 1 2 1 2 1 1 1 2 ... $ id : num 1 2 3 3 4 4 5 6 7 7 ... > Surv(heart$start, heart$stop, heart$event) [1] ( 0.0, 50.0] ( 0.0, 6.0] ( 0.0, 1.0+] [4] ( 1.0, 16.0] ( 0.0, 36.0+] ( 36.0, 39.0] [7] ( 0.0, 18.0] ( 0.0, 3.0] ( 0.0, 51.0+] [10] ( 51.0, 675.0] ( 0.0, 40.0] ( 0.0, 85.0] [13] ( 0.0, 12.0+] ( 12.0, 58.0] ( 0.0, 26.0+] [16] ( 26.0, 153.0] ( 0.0, 8.0] ( 0.0, 17.0+] [19] ( 17.0, 81.0] ( 0.0, 37.0+] ( 37.0,1387.0] [22] ( 0.0, 1.0] ( 0.0, 28.0+] ( 28.0, 308.0] [25] ( 0.0, 36.0] ( 0.0, 20.0+] ( 20.0, 43.0] [28] ( 0.0, 37.0] ( 0.0, 18.0+] ( 18.0, 28.0] [31] ( 0.0, 8.0+] ( 8.0,1032.0] ( 0.0, 12.0+] [34] ( 12.0, 51.0] ( 0.0, 3.0+] ( 3.0, 733.0] [37] ( 0.0, 83.0+] ( 83.0, 219.0] ( 0.0, 25.0+] [40] ( 25.0,1800.0+] ( 0.0,1401.0+] ( 0.0, 263.0] [43] ( 0.0, 71.0+] ( 71.0, 72.0] ( 0.0, 35.0] [46] ( 0.0, 16.0+] ( 16.0, 852.0] ( 0.0, 16.0] [49] ( 0.0, 17.0+] ( 17.0, 77.0] ( 0.0, 51.0+] [52] ( 51.0,1587.0+] ( 0.0, 23.0+] ( 23.0,1572.0+] [55] ( 0.0, 12.0] ( 0.0, 46.0+] ( 46.0, 100.0] [58] ( 0.0, 19.0+] ( 19.0, 66.0] ( 0.0, 4.5+] [61] ( 4.5, 5.0] ( 0.0, 2.0+] ( 2.0, 53.0] [64] ( 0.0, 41.0+] ( 41.0,1408.0+] ( 0.0, 58.0+] [67] ( 58.0,1322.0+] ( 0.0, 3.0] ( 0.0, 2.0] [70] ( 0.0, 40.0] ( 0.0, 1.0+] ( 1.0, 45.0] [73] ( 0.0, 2.0+] ( 2.0, 996.0] ( 0.0, 21.0+] [76] ( 21.0, 72.0] ( 0.0, 9.0] ( 0.0, 36.0+] [79] ( 36.0,1142.0+] ( 0.0, 83.0+] ( 83.0, 980.0] [82] ( 0.0, 32.0+] ( 32.0, 285.0] ( 0.0, 102.0] [85] ( 0.0, 41.0+] ( 41.0, 188.0] ( 0.0, 3.0] [88] ( 0.0, 10.0+] ( 10.0, 61.0] ( 0.0, 67.0+] [91] ( 67.0, 942.0+] ( 0.0, 149.0] ( 0.0, 21.0+] [94] ( 21.0, 343.0] ( 0.0, 78.0+] ( 78.0, 916.0+] [97] ( 0.0, 3.0+] ( 3.0, 68.0] ( 0.0, 2.0] [100] ( 0.0, 69.0] ( 0.0, 27.0+] ( 27.0, 842.0+] [103] ( 0.0, 33.0+] ( 33.0, 584.0] ( 0.0, 12.0+] [106] ( 12.0, 78.0] ( 0.0, 32.0] ( 0.0, 57.0+] [109] ( 57.0, 285.0] ( 0.0, 3.0+] ( 3.0, 68.0] [112] ( 0.0, 10.0+] ( 10.0, 670.0+] ( 0.0, 5.0+] [115] ( 5.0, 30.0] ( 0.0, 31.0+] ( 31.0, 620.0+] [118] ( 0.0, 4.0+] ( 4.0, 596.0+] ( 0.0, 27.0+] [121] ( 27.0, 90.0] ( 0.0, 5.0+] ( 5.0, 17.0] [124] ( 0.0, 2.0] ( 0.0, 46.0+] ( 46.0, 545.0+] [127] ( 0.0, 21.0] ( 0.0, 210.0+] (210.0, 515.0+] [130] ( 0.0, 67.0+] ( 67.0, 96.0] ( 0.0, 26.0+] [133] ( 26.0, 482.0+] ( 0.0, 6.0+] ( 6.0, 445.0+] [136] ( 0.0, 428.0+] ( 0.0, 32.0+] ( 32.0, 80.0] [139] ( 0.0, 37.0+] ( 37.0, 334.0] ( 0.0, 5.0] [142] ( 0.0, 8.0+] ( 8.0, 397.0+] ( 0.0, 60.0+] [145] ( 60.0, 110.0] ( 0.0, 31.0+] ( 31.0, 370.0+] [148] ( 0.0, 139.0+] (139.0, 207.0] ( 0.0, 160.0+] [151] (160.0, 186.0] ( 0.0, 340.0] ( 0.0, 310.0+] [154] (310.0, 340.0+] ( 0.0, 28.0+] ( 28.0, 265.0+] [157] ( 0.0, 4.0+] ( 4.0, 165.0] ( 0.0, 2.0+] [160] ( 2.0, 16.0] ( 0.0, 13.0+] ( 13.0, 180.0+] [163] ( 0.0, 21.0+] ( 21.0, 131.0+] ( 0.0, 96.0+] [166] ( 96.0, 109.0+] ( 0.0, 21.0] ( 0.0, 38.0+] [169] ( 38.0, 39.0+] ( 0.0, 31.0+] ( 0.0, 11.0+] [172] ( 0.0, 6.0]
2.survreg
拟合参数生存回归模型。 这些是用于时间变量的任意变换的位置尺度模型; 最常见的情况使用对数变换，建立加速失效时间模型。
survreg(formula, data, weights, subset,
na.action, dist="weibull", init=NULL, scale=0,
control,parms=NULL,model=FALSE, x=FALSE,
y=TRUE, robust=FALSE, score=FALSE, ...)
dist
y变量的假设分布。"weibull", "exponential", "gaussian", "logistic","lognormal" and "loglogistic"。
scale
可选的固定值。如果设置<=0，scale将被估计
control
控制值列表，参考survreg.control()
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