[R] question of elimination drawn lines on competing risk graph

David Winsemius dwinsemius at comcast.net
Fri Feb 18 21:01:29 CET 2011


On Feb 18, 2011, at 2:10 PM, Gerard Smits wrote:

> Hi All,
>
> I am using the package, cmprisk, to plot competing risks.  In my  
> case, I have four lines showing risk of going on dialysis (by a lab  
> test [fgf-23] in quartiles), where the 4 lower lines are for the  
> competing risk of death.
>
> I am trying to edit the function to just plot the upper curves (and  
> not show the lower (competing) risk death curves.   I am not  
> especially facile in editing this code, so would appreciate any help  
> as to where I should be making changes.

Two problems:
A) You don't provide the data or something that give a reasonable look  
at the data.
B) When I use that function with the dataset provided on the webpage  
maintained by the authors, it appears to me that it simply plots  
cuminc for each group separately. (They are al crossingeach other,  
hence there is no summary curve "on top".)  So you would just remove  
whatever groups were superfluous prior to forwarding to the plotting  
function. (Hence no need to edit anything.)

-- 
David

>
> Hope OK to include the function code.
>
> Thanks.
>
> Gerard
>
>
>
> require(cmprsk)
>
> #############################################################################
> #                                                                           #
> #                 CUMULATIVE INCIDENCE CURVES IN  
> R                          #
> #                                                                           #
> # Written by Luca  
> Scrucca                                                   #
> #                                                                           #
> #  
> Reference 
> :                                                                #
> # Scrucca L., Santucci A., Aversa F. (2007) Competing risks analysis  
> using  #
> #   R: an easy guide for clinicians. Bone Marrow Transplantation,  
> 40,       #
> #    
> 381 
> --387.                                                               #
> #############################################################################
> # ver. 1.1 Feb 2008
> #          - allow group to be missing
> #          - if t is provided both computation and plots use t as  
> time points
> #          - allow col, lwd to be used for curves with confidence  
> bands
> #          - fix some bugs in the legend
> #          - added help on source code
> # ver. 1.0 May 2007
> #          - Version appearing in the BMT paper
> #############################################################################
> #
> # Usage:
> #
> #   CumIncidence(ftime, fstatus, group, t, strata, rho = 0, cencode  
> = 0,
> #             	 subset, na.action = na.omit, level,
> #                xlab = "Time", ylab = "Probability",
> #                col, lty, lwd, digits = 4)
> #
> # Arguments:
> #
> # ftime   = failure time variable.
> # fstatus = variable with distinct codes for different causes of
> #           failure and also a distinct code for censored  
> observations.
> # group	  = estimates will be calculated within groups given by  
> distinct
> #           values of this variable. Tests will compare these  
> groups. If
> #           missing then treated as all one group (no test  
> statistics).
> # t =       a vector of time points where the cumulative incidence  
> function
> #           should be evaluated.
> # strata =  stratification variable. Has no effect on estimates. Tests
> #           will be stratified on this variable. (all data in 1  
> stratum,
> #           if missing).
> # rho =     power of the weight function used in the tests. By  
> default is
> #           set to 0.
> # cencode = value of fstatus variable which indicates the failure time
> #           is censored.
> # subset =  a logical vector specifying a subset of cases to include  
> in
> #           the analysis.
> # na.action=a function specifying the action to take for any cases
> #           missing any of ftime, fstatus, group, strata, or subset.
> #           By default missing cases are omitted.
> # level =   a value in the range [0,1] specifying the level for  
> pointwise
> #           confidence bands.
> # xlab =    text for the x-axis label.
> # ylab =    text for the y-axis label.
> # col =     color(s) used for plotting curves (see plot.default).
> # lty =     line type(s) used for plotting curves (see plot.default).
> # lwd =     line width(s) used for plotting curves (see plot.default).
> # digits =  number of significant digits used for printing values. By
> #           default set at 4.
> #
> #############################################################################
>
> "CumIncidence" <- function(ftime, fstatus, group, t, strata, rho = 0,
> 	                         cencode = 0, subset, na.action = na.omit,  
> level,
> 	                         xlab = "Time", ylab = "Probability",
> 	                         col, lty, lwd, digits = 4)
> {
>  # check for the required package
>  if(!require("cmprsk"))
>    { stop("Package `cmprsk' is required and must be installed.\n
>           See help(install.packages) or write the following command  
> at prompt
>           and then follow the instructions:\n
>> install.packages(\"cmprsk\")") }
>  # collect data
>  mf  <- match.call(expand.dots = FALSE)
>  mf[[1]] <- as.name("list")
>  mf$t <- mf$digits <- mf$col <- mf$lty <- mf$lwd <- mf$level <-
>  mf$xlab <- mf$ylab <- NULL
>  mf <- eval(mf, parent.frame())
>  g <- max(1, length(unique(mf$group)))
>  s <- length(unique(mf$fstatus))
>  if(missing(t))
>    { time <- pretty(c(0, max(mf$ftime)), 6)
>      ttime <- time <- time[time < max(mf$ftime)] }
>  else { ttime <- time <- t }
>  # fit model and estimates at time points
>  fit   <- do.call("cuminc", mf)
>  tfit <- timepoints(fit, time)
>  # print result
>  cat("\n+", paste(rep("-", 67), collapse=""), "+", sep ="")
>  cat("\n| Cumulative incidence function estimates from competing  
> risks data |")
>  cat("\n+", paste(rep("-", 67), collapse=""), "+\n", sep ="")
>  tests <- NULL
>  if(g > 1)
>    { tests <- fit$Tests
> 	    colnames(tests) <- c("Statistic", "p-value", "df")
>      cat("Test equality across groups:\n")
>      print(tests, digits = digits) }
>  cat("\nEstimates at time points:\n")
>  print(tfit$est, digits = digits)
>  cat("\nStandard errors:\n")
>  print(sqrt(tfit$var), digits = digits)
>  #
>  if(missing(level))
>    { # plot cumulative incidence functions
>      if(missing(t))
>        { time <- sort(unique(c(ftime, time)))
>          x <- timepoints(fit, time) }
>      else x <- tfit
>      col <- if(missing(col)) rep(1:(s-1), rep(g,(s-1))) else col
>      lty <- if(missing(lty)) rep(1:g, s-1) else lty
>      lwd <- if(missing(lwd)) rep(1, g*(s-1)) else lwd
>      matplot(time, base::t(x$est), type="s", ylim = c(0,1),
>              xlab = xlab, ylab = ylab, xaxs="i", yaxs="i",
>              col = col, lty = lty, lwd = lwd)
>      legend("topleft", legend =  rownames(x$est), x.intersp = 2,
>             bty = "n", xjust = 1, col = col, lty = lty, lwd = lwd,  
> cex=.75)
>      out <- list(test = tests, est = tfit$est, se = sqrt(tfit$var))
>    }
>  else
>    { if(level < 0 | level > 1)
>        error("level must be a value in the range [0,1]")
>      # compute pointwise confidence intervals
>      oldpar <- par(ask=TRUE)
>      on.exit(par(oldpar))
>      if(missing(t))
>        { time <- sort(unique(c(ftime, time)))
>          x <- timepoints(fit, time) }
>      else x <- tfit
>      z <- qnorm(1-(1-level)/2)
>      lower <- x$est ^ exp(-z*sqrt(x$var)/(x$est*log(x$est)))
>      upper <- x$est ^ exp(z*sqrt(x$var)/(x$est*log(x$est)))
>      col <- if(missing(col)) rep(1:(s-1), rep(g,(s-1)))
>             else             rep(col, g*(s-1))
>      lwd <- if(missing(lwd)) rep(1, g*(s-1))
>             else             rep(lwd, g*(s-1))
>      # plot pointwise confidence intervals
>      for(j in 1:nrow(x$est))
>         { matplot(time, cbind(x$est[j,], lower[j,], upper[j,]),  
> type="s",
>                   xlab = xlab, ylab = ylab, xaxs="i", yaxs="i",
>                   ylim = c(0,1), col = col[j], lwd = lwd[j], lty =  
> c(1,3,3))
>           legend("topleft", legend =  rownames(x$est)[j], bty = "n",  
> xjust = 1) }
>      # print pointwise confidence intervals
>      i <- match(ttime, time)
>      ci <- array(NA, c(2, length(i), nrow(lower)))
>      ci[1,,] <- base::t(lower[,i])
>      ci[2,,] <- base::t(upper[,i])
>      dimnames(ci) <- list(c("lower", "upper"), ttime, rownames(lower))
>      cat(paste("\n", level*100, "% pointwise confidence intervals:\n 
> \n", sep=""))
>      print(ci, digits = digits)
>      out <- list(test = tests, est = x$est, se = sqrt(tfit$var), ci  
> = ci)
>    }
>  # return results
>  invisible(out)
> }
>
>
>
>
> 	[[alternative HTML version deleted]]
>
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> and provide commented, minimal, self-contained, reproducible code.

David Winsemius, MD
West Hartford, CT



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