CIBMTR {SemiCompRisks} | R Documentation |
We provide a dataset with five covariates from a study of acute graft-versus-host (GVHD) disease with 9651 patients who underwent first allogeneic hematopoietic cell transplant. We also provide an algorithm to simulate semi-competing risks outcome data.
data("CIBMTR")
A data frame with 9651 observations on the following 5 variables.
sexP
patient sex: M
-Male, F
-Female
ageP
patient age: LessThan10
, 10to19
, 20to29
, 30to39
, 40to49
, 50to59
, 60plus
dType
disease type: AML
-Acute Myeloid Leukemia, ALL
-Acute Lymphoblastic Leukemia, CML
-Chronic Myeloid Leukemia, MDS
-Myelodysplastic Syndrome
dStatus
disease stage: Early
-early, Int
-intermediate, Adv
-advanced
donorGrp
human leukocyte antigen compatibility: HLA_Id_Sib
-identical sibling, 8_8
-8/8, 7_8
-7/8
See Examples below for an algorithm to simulate semi-competing risks outcome data.
Center for International Blood and Bone Marrow Transplant Research
Lee, C., Lee, S.J., Haneuse, S. (2017+). Time-to-event analysis when the event is defined on a finite time interval. under review.
data(CIBMTR_Params) data(CIBMTR) ## CREATING DUMMY VARIABLES ## # Sex (M: reference) CIBMTR$sexP <- as.numeric(CIBMTR$sexP)-1 # Age (LessThan10: reference) CIBMTR$ageP20to29 <- as.numeric(CIBMTR$ageP=="20to29") CIBMTR$ageP30to39 <- as.numeric(CIBMTR$ageP=="30to39") CIBMTR$ageP40to49 <- as.numeric(CIBMTR$ageP=="40to49") CIBMTR$ageP50to59 <- as.numeric(CIBMTR$ageP=="50to59") CIBMTR$ageP60plus <- as.numeric(CIBMTR$ageP=="60plus") # Disease type (AML: reference) CIBMTR$dTypeALL <- as.numeric(CIBMTR$dType=="ALL") CIBMTR$dTypeCML <- as.numeric(CIBMTR$dType=="CML") CIBMTR$dTypeMDS <- as.numeric(CIBMTR$dType=="MDS") # Disease status (Early: reference) CIBMTR$dStatusInt <- as.numeric(CIBMTR$dStatus=="Int") CIBMTR$dStatusAdv <- as.numeric(CIBMTR$dStatus=="Adv") # HLA compatibility (HLA_Id_Sib: reference) CIBMTR$donorGrp8_8 <- as.numeric(CIBMTR$donorGrp=="8_8") CIBMTR$donorGrp7_8 <- as.numeric(CIBMTR$donorGrp=="7_8") # Covariate matrix x <- CIBMTR[,c("sexP","ageP20to29","ageP30to39","ageP40to49","ageP50to59","ageP60plus", "dTypeALL","dTypeCML","dTypeMDS","dStatusInt","dStatusAdv","donorGrp8_8","donorGrp7_8")] # Set the parameter values beta1 <- CIBMTR_Params$beta1.true beta2 <- CIBMTR_Params$beta2.true beta3 <- CIBMTR_Params$beta3.true alpha1 <- CIBMTR_Params$alpha1.true alpha2 <- CIBMTR_Params$alpha2.true alpha3 <- CIBMTR_Params$alpha3.true kappa1 <- CIBMTR_Params$kappa1.true kappa2 <- CIBMTR_Params$kappa2.true kappa3 <- CIBMTR_Params$kappa3.true theta <- CIBMTR_Params$theta.true set.seed(1405) simCIBMTR <- simID(id=NULL, x, x, x, beta1, beta2, beta3, alpha1, alpha2, alpha3, kappa1, kappa2, kappa3, theta, SigmaV.true=NULL, cens=c(365,365)) names(simCIBMTR) <- c("time1", "event1", "time2", "event2") CIBMTR <- cbind(simCIBMTR, CIBMTR) head(CIBMTR)