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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,55 @@ cria.tau <- function(data){ # Giolo, Suely Ruiz. "Turnbull's nonparametric estimator for interval-censored data'." # Department of Statistics, Federal University of Paraná (2004): 1-10. l <- data$left r <- data$right tau <- sort(unique(c(l,r[is.finite(r)]))) return(tau) } S.ini <- function(tau){ m<-length(tau) ekm<-survfit(Surv(tau[1:m-1],rep(1,m-1))) So<-c(1,ekm$surv) p <- -diff(So) return(p) } cria.A <- function(data,tau){ tau12 <- cbind(tau[-length(tau)],tau[-1]) interv <- function(x,inf,sup) ifelse(x[1]>=inf & x[2]<=sup,1,0) A <- apply(tau12,1,interv,inf=data$left,sup=data$right) id.lin.zero <- which(apply(A==0, 1, all)) if(length(id.lin.zero)>0) A <- A[-id.lin.zero, ] return(A) } Turnbull <- function(p, A, data, eps=1e-3, iter.max=200, verbose=FALSE){ n<-nrow(A) m<-ncol(A) Q<-matrix(1,m) iter <- 0 repeat { iter <- iter + 1 diff<- (Q-p) maxdiff<-max(abs(as.vector(diff))) if (verbose) print(maxdiff) if (maxdiff<eps | iter>=iter.max) break Q<-p C<-A%*%p p<-p*((t(A)%*%(1/C))/n) } cat("Iterations = ", iter,"\n") cat("Max difference = ", maxdiff,"\n") cat("Convergence criteria: Max difference < 1e-3","\n") dimnames(p)<-list(NULL,c("P Estimate")) surv<-round(c(1,1-cumsum(p)),digits=5) right <- data$right if(any(!(is.finite(right)))){ t <- max(right[is.finite(right)]) return(list(time=tau[tau<t],surv=surv[tau<t])) } else return(list(time=tau,surv=surv)) } This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,69 @@ # Giolo, Suely Ruiz. "Turnbull's nonparametric estimator for interval-censored data'." # Department of Statistics, Federal University of Paraná (2004): 1-10. dat <-structure(list(left = c(1, 1, 1, 28, 34, 34, 35, 36, 36, 3, 3, 7, 9, 9, 9, 10, 11, 10, 13, 15, 15, 16, 17, 18, 21, 22, 23, 25, 25, 25, 25, 26, 26, 26, 26, 27, 29, 31, 32, 32, 32, 32, 32, 32, 32, 32, 1, 1, 28, 28, 34, 37, 37, 2, 2, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 6, 6, 7, 8, 8, 8, 8, 9, 9, 9, 10, 10, 11, 12, 38, 14, 15, 15, 19, 19, 20, 21, 21, 23, 23, 24, 24, 30, 33), right = c(32, 33, 30, 2, 3, 2, 1, 7, 5, 6, 9, NA, NA, 16, 16, NA, 23, 17, NA, NA, NA, 25, 27, 22, NA, NA, NA, 28, 29, NA, NA, 28, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 13, 30, 34, 33, 33, 3, 12, 23, 8, 4, NA, 8, NA, 11, 11, NA, 26, NA, NA, 8, 10, 13, 15, 11, 15, 31, 18, 14, 17, 16, 15, 21, NA, 21, NA, 20, 19, 22, 24, NA, NA, 27, NA, NA, NA, 26, 29, NA), ther = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), cens = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1)), .Names = c("left", "right", "ther", "cens"), row.names = 2:95, class = "data.frame") #=============================================== #Survival curves using intervals - Figure 1 #=============================================== require(survival) source("Turnbull.R") # Turnbull.R dat1 <- dat[dat$ther==1,] dat1$right[is.na(dat1$right)] <- Inf tau <- cria.tau(dat1) p <- S.ini(tau=tau) A <- cria.A(data=dat1,tau=tau) tb1 <- Turnbull(p,A,dat1) tb1 dat1 <- dat[dat$ther==0,] dat1$right[is.na(dat1$right)] <- Inf tau <- cria.tau(dat1) p <- S.ini(tau=tau) A <- cria.A(data=dat1,tau=tau) tb2 <- Turnbull(p,A,dat1) tb2 plot(tb1$time,tb1$surv,lty=1, col = 4,type="s",ylim=c(0,1),xlim=range(c(0,60)), xlab="Tempos (meses)",ylab="S(t)") lines(tb2$time,tb2$surv,lty=4,col=2,type="s") legend(1,0.3,lty=c(1,4),col=c(4,2),c("Radioterapia","Radioterapia + Quimioterapia"), bty="n",cex=0.8) #=============================================== #Survival curves using midpoints - Figure 2 #=============================================== p <-dat$left+((dat$right-dat$left)/2) pm <-ifelse(is.finite(p),p,dat$left) cens <- ifelse(is.finite(p),1,0) ekm<-survfit(Surv(pm,cens)~ther,type=c("kaplan-meier"),data=dat) plot(tb1$time,tb1$surv,lty=1,type="s",col=4,ylim=c(0,1),xlim=c(0,50),xlab="Tempos (meses)",ylab="S(t)") lines(tb2$time,tb2$surv,lty=1,col=2,type="s") lines(ekm[1]$time,ekm[1]$surv,type="s",col=2,lty=2) lines(ekm[2]$time,ekm[2]$surv,type="s",col=4,lty=2) legend(3,0.30,lty=2,col=4, "Radiotherapy using midpoints", bty="n",cex=0.8) legend(3,0.25,lty=1,col=4, "Radiotherapy using intervals", bty="n",cex=0.8) legend(3,0.2,lty=2,col=2,"Radio + Chemotherapy using midpoints", bty="n",cex=0.8) legend(3,0.15,lty=1,col=2,"Radio + Chemotherapy using intervals", bty="n",cex=0.8)