# For references, see Olive (2020), Survival Analysis. # data sets are listed alphabetically as # Christiansen and Morris (1996), transplant data # Collett (2003, p. 335-6), ovarian survival data # Collett (2003, p. 10), prostate data # Leemis (1995, p. 249-250) and Lawless (1982, p. 286), lung survival data # McGilchrist and Aisbett (1991), kidney data ## Christiansen and Morris (1996), transplant data #Data on patient deaths within 30 days of surgery in 131 U.S. #hospitals. See Christiansen and Morris, Bayesian Biostatistics, D. #Berry and D. Stangl, editors, 1996, Marcel Dekker, Inc. #Data on 131 heart transplant hospitals in the US. The transplants #took place during a 27 month period from October 1987 through December #1989. The columns are: obs = hospital #, e = expected # #of deaths within 30 days of the transplant surgeries, z = number of #deaths within 30 days of surgery, n = # of patients receiving heart #transplant within this time period. (Christiansen and Morris, Bayesian #Biostatistics, D. Berry and D. Stangl, editors, 1996.) The patient #level data used to create this data set was provided by the United #Network for Organ Sharing, 1100 Boulders Parkway, Suite 500, P.O. Box #13770, Richmond, VA, 23225. #The following data may be used for non-commercial purposes and can be #distributed freely. If you use the data, please acknowledge StatLib, #the United Network for Organ Sharing, and Christiansen and Morris, #1996. ## transplantdata, n=131 e<-c(0.057,0.064,0.064,0.066,0.462,0.086,0.114,0.117,0.118,0.119,0.126,0.231,0.261,0.211,0.216,0.218,0.648,0.143,0.144,0.21,0.244,0.53,0.796,1.622,0.561, 0.762,1.222,0.513,0.532,0.936,0.503,0.564,0.572,0.583,0.722,0.771,1.873,0.532,0.584,0.672,0.722,0.904,1.236,0.95,1.405,0.776,1.013,0.739,1.77,0.821,1.115, 1.164,1.164,1.303,1.774,3.585,1.193,1.213,1.232,1.517,1.52,1.862,1.888,1.247,1.381,1.643,1.66,1.827,1.486,1.593,2.265,1.524,1.759,1.309,1.529,1.677,1.654, 1.785,1.979,1.767,2.465,1.75,2.458,2.383,2.717,2.282,2.115,2.852,2.856,3.174,2.369,2.557,3.859,2.641,2.741,3.055,3.513,2.728,3.354,3.814,4.014,2.612,2.815, 4.294,3.45,3.628,4.219,3.932,4.082,4.203,4.022,4.636,5.571,6.436,5.344,4.445,4.705,5.039,6.043,5.121,11.26,5.789,6.044,5.569,6.13,6.249,7.002,7.851,9.573, 12.05,12.131) z<-c(0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,0,1,0,2,1,0,3,2,4,2,0,1,4,0,1,1,0,2,0,0,2,4,0,0,2,1,1,0,0,1,3,0,0,1,0,2,3,0,0,3,1,1,1,1,4,3,3,1,0,2,2,4,4,3,2,4,1,3,0, 4,1,2,3,4,4,2,2,4,2,3,0,0,2,5,5,1,1,3,1,1,3,1,2,6,0,2,2,1,2,8,6,1,6,4,1,3,5,2,4,3,4,5,2,6,8,5,0,6,8,7,3,3,9,7,18,17) n<-c(1,1,1,1,1,2,2,2,2,2,2,2,2,3,3,3,3,4,4,4,4,6,6,6,7,7,7,8,8,8,9,9,9,9,9,9,9,10,10,10,10,11,11,12,12,13,13,14,14,15,15,15,15,16,16,16,17,17,17,17,17,17,17, 18,18,18,18,19,20,20,20,21,21,22,24,24,25,25,25,26,26,27,27,28,28,32,33,33,33,33,35,35,35,37,38,38,38,40,40,40,41,42,43,43,44,45,45,47,47,48,51,51,56,57,60, 61,61,61,68,69,69,73,74,75,75,79,91,99,104,133,152) # Collett (2003, p. 335-6), ovarian survival data time <- c(156, 1040, 59, 421, 329, 769, 365, 770, 1227, 268, 475, 1129, 464, 1206, 638, 563, 1106, 431, 855, 803, 115, 744, 477, 448, 353, 377) status <- c(1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0) treat <- c(1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0) age <- c(66, 38, 72, 53, 43, 59, 64, 57, 59, 74, 59, 53, 56, 44, 56, 55, 44, 50, 43, 39, 74, 50, 64, 56, 63, 58) ovar <- cbind(time,status,treat,age) rm(time,status,treat,age) #Collett (2003, p. 10), prostate data treat <-c(1,2,2,1,2,1,1,1,2,1,1,2,2,2,1,1,2,2,2,2,1,1,2,1,2,2,1,1,1, 2,2,2,2,1,2,2,1,1) time<-c(65,61,60,58,51,51,14,43,16,52,59,55,68,51,2,67,66,66,28,50,69,67,65,24,45, 64,61,26,42,57,70,5,54,36,70,67,23,62) status<- 0*1:38; status[c(7,20,21,28,29,34)]<-1 age<-c(67,60,77,64,65,61,73,60,73,73,77,74,71,65,76,70,70,70, 75,68,60,71,51,71,72,73,75,72,57,72,72,74,51,72,71,73,68,63) serum<-c(13.4,14.6,15.6,16.2,14.1,13.5,12.4,13.6,13.8,11.7,12.0,14.3,14.5,14.4, 10.7,14.7,16.0,14.5,13.7,12.0,16.1,15.6,11.8,13.7,11.0,14.2,13.7,15.3,13.9,14.6, 13.8,15.1,15.8,16.4,13.6,13.8,12.5,13.2) size<-c(34,4,3,6,21,8,18,7,8,5,7,7,19,10,8,7,8,15, 19,20,26,8,2,10,4,4,10,37,24,8,3,3,7,4,2,7,2,3) gleas<-c(8,10,8,9,9,8,11,9,9,9,10,10,9,9,9,9,9,11, 10,11,9,8,6,9,8,6,12,11,12,10,9,9,8,9,10,8,8,8) prostate <- cbind(treat,time,status,age,serum,size,gleas) pros<-as.data.frame(prostate) # Leemis (1995, p. 249-250) and Lawless (1982, p. 286), lung survival data time <- c(411,126,118,92,8,25,11,54,153,16,56,21,287,10,8,12,177, 12,200,250,100,999,231,991,1,201,44,15,103,2,20,51,18,90,84,164, 19,43,340,231) status <- 1 + 0* 1:40 status[c(6,23,29)] <- 0 perf <- c(70,60,70,40,40,70,70,80,60,30,80,40,60,40,20,50,50,40,80, 70,60,90,50,70,20,80,60,50,70,40,30,30,40,60,80,70,30,60,80,70) age <- c(64,63,65,69,63,48,48,63,63,53,43,55,66,67,61,63,66,68,41,53, 37,54,52,50,65,52,70,40,36,44,54,59,69,50,62,68,39,49,64,67) ttoent <- c(5,9,11,10,58,9,11,4,14,4,12,2,25,23,19,4,16,12,12,8,13, 12,8,7,21,28,13,13,22,36,9,87,5,22,4,15,4,11,10,18) size <- 0*1:40 size[c(1:7,22:28)] <- 1 type <- 0*1:40 type[c(8:14,29:32)] <- 1 ttype <- 0*1:40 ttype[c(15,16,33:35)] <- 1 trt <- 0*1:40 trt[1:21] <- 1 alung <- cbind(time,status,perf,age,ttoent,size,type,ttype,trt) rm(time,status,perf,age,ttoent,size,type,ttype,trt) ##McGilchrist and Aisbett (1991), kidney data #From: McGilchrist and Aisbett, Biometrics 47, 461-66, 1991 #Data on the recurrence times to infection, at the point of #insertion of # the catheter, for kidney patients using portable dialysis #equipment. # Catheters may be removed for reasons other than infection, in #which case #the observation is censored. Each patient has exactly 2 #observations. #The data set has been used by several authors to illustrate #random effects #("frailty") models for survival data. However, any non-zero #estimate of the #random effect is almost entirely due to one outlier, subject 21. #Variables: patient, time, status, age, # sex (1=male, 2=female), # disease type (0=Glomerulo Nephritis, 1=Acute #Nephritis, # 2=Polycystic Kidney Disease, 3=Other) # author's estimate of the frailty ##kidneydata, n=76 patient<-c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14, 15,15,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27, 28,28,29,29,30,30,31,31,32,32,33,33,34,34,35,35,36,36,37,37,38,38) time<-c(8,16,23,13,22,28,447,318,30,12,24,245,7,9,511,30,53,196,15,154,7,333,141,8, 96,38,149,70,536,25,17,4,185,177,292,114,22,159,15,108,152,562,402,24, 13,66,39,46,12,40,113,201,132,156,34,30,2,25,130,26,27,58,5,43,152,30,190,5,119,8,54,16,6,78,63,8) status<-c(1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0,1,0,1,0,1,1,1,1, 0,0,1,0,1,1,1,0,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,1,1,0,0,0,1,1,0) age<-c(28,28,48,48,32,32,31,32,10,10,16,17,51,51,55,56,69,69,51,52,44,44,34,34, 35,35,42,42,17,17,60,60,60,60,43,44,53,53,44,44,46,47,30,30,62,63,42,43,43, 43,57,58,10,10,52,52,53,53,54,54,56,56,50,51,57,57,44,45,22,22,42,42,52,52,60,60) sex<-c(1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,2,2,1,1,2,2,2,2,2,2,2,2,2,2,1,1,2,2,2,2,2, 2,2,2,1,1,2,2,2,2,2,2,1,1,2,2,2,2,2,2,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2, 2,2,2,1,1) diseasetype<-c(3,3,0,0,3,3,3,3,3,3,3,3,0,0,0,0,1,1,0,0,1,1,3,3,1,1,1,1,3,3,1,1, 3,3,3,3,0,0,3,3,2,2,3,3,1,1,1,1,1,1,1,1,0,0,1,1,0,0,0,0,1,1,1,1,2,2,0, 0,3,3,3,3,2,2,2,2)