# Plot Method for 'bshazard' A plot of hazard rate is produced. The overall option allows to plot an hazard rate for each covariate value (assuming proportional hazard). ```r ## S3 method for class 'bshazard' plot(x, conf.int = T, overall = T, col = 1, lwd = 1, lty = 1, xlab = "Time", ylab = "Hazard rate",border=NA,col.fill="lightgrey",...) ``` ## Arguments - `x`: the result of a call to the bshazard function. - `conf.int`: Determines whether confidence intervals will be plotted. The default is to do so if there is only 1 curve, i.e., no strata. - `overall`: Determines whether an overall curve is plotted (default overall=T) or a curve for each covariate value in the data (overall=F). It works only if there are covariates. - `col`: a vector of integers specifying colors for each curve. The default value is 1. - `lwd`: a vector of integers specifying line width for each curve. The default value is 1. - `lty`: a vector of integers specifying line types for each curve. The default value is 1. - `xlab`: label given to the x-axis. - `ylab`: label given to the y-axis. - `border`: the color to draw the border. The deafult value is NA. - `col.fill`: the color for filling the polygon. The default is lightgrey. - ``...``: other arguments that will be passed forward to the underlying plot method, such as xlab or ylab. ## See Also bshazard,summary.bshazard,print.bshazard ## Examples ```r fit<-bshazard(Surv(time, status==2) ~ 1,data=lung) plot(fit) #Example to graphically evaluate the Markov assumpion in an illness-death model ### data simulated under EXTENDED SEMI-MARKOV model set.seed(123) n <- 500 beta<-log(3) R <- runif(n, min = 0, max =2) cat <- cut(R, breaks = seq(0,2,0.5), labels = seq(1,4,1)) T12 <- 1/0.2*( (-log(runif(n, min = 0, max = 1))) / (exp(beta*R)))**(1/0.6) C <- runif(n, min =0, max = 10) T12obs <- pmin(T12, C) status <- ifelse(T12obs < C, 1, 0) T012obs <- R+T12obs #R: simulated time to illness #cat: time to illness categorised in 4 classes #T12obs: time from illness to death or censoring #T012obs: time from origin to death or censoring #status: indicator of death (1=death,0=censoring) # Hazard estimate in the Clock Reset scale (time from illness) by time to illness class fit <- bshazard(Surv(T12obs[cat == 1],status[cat==1]) ~ 1) plot(fit,conf.int=FALSE,xlab='Time since illness',xlim=c(0,5),ylim=c(0,10),lwd=2,col=rainbow(1)) for(i in 2:4) { fit <- bshazard(Surv(T12obs[cat == i],status[cat==i]) ~ 1) lines(fit$time, fit$hazard, type = 'l', lwd = 2, col = rainbow(4)[i]) } legend("top",title="Time to illness",c("<=0.5","0.5-|1","1-|1.5","1.5-|2"),col=c(rainbow(4)),lwd =2) # Hazard estimate in the Clock Forward scale (time from origin) by time to illness class fit <- bshazard(Surv(R[cat == 1],T012obs[cat == 1],status[cat==1]) ~ 1) plot(fit,conf.int=FALSE,xlab='Time since origin',xlim=c(0,5),ylim=c(0,10),lwd=2,col=rainbow(1)) for(i in 2:4) { fit <- bshazard(Surv(R[cat == i],T012obs[cat == i],status[cat==i]) ~ 1) lines(fit$time, fit$hazard, type = 'l', lwd = 2, col = rainbow(4)[i]) } legend("top",title="Time to illness",c("<=0.5","0.5-|1","1-|1.5","1.5-|2"),col=c(rainbow(4)),lwd =2) #Alternatively an adjusted estimate can be performed, assuming proportionl hazard # this computation can be time consuming! ## Not run: fit.adj <- bshazard(Surv(T12obs,status) ~ cat ) plot(fit.adj,overall=FALSE, xlab = 'Time since illness',col=rainbow(4),lwd=2, xlim = c(0,5), ylim = c(0,10)) legend("top",title="Time to illness",c("<=0.5","0.5-|1","1-|1.5","1.5-|2"),col=c(rainbow(4)),lwd =2) ## End(Not run) ### A more general setting with examples of Markov assumption evaluation can be found ### in Bernasconi et al. Stat in Med 2016 ```

plot.bshazard function