# Center for International Blood and Bone Marrow Transplant Research (CIBMTR) data 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 ```r data("CIBMTR") ``` ## Format 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 ## Details See Examples below for an algorithm to simulate semi-competing risks outcome data. ## Source Center for International Blood and Bone Marrow Transplant Research ## References Lee, C., Lee, S.J., Haneuse, S. (2017+). Time-to-event analysis when the event is defined on a finite time interval. **under review**. ## See Also `CIBMTR_Params` ## Examples ```r 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) ```

CIBMTR dataset