stdca.r

stdca <- function(data, outcome, ttoutcome, timepoint, predictors, xstart=0.01, xstop=0.99, xby=0.01, 
                ymin=-0.05, probability=NULL, harm=NULL,graph=TRUE, intervention=FALSE, 
                interventionper=100, smooth=FALSE,loess.span=0.10,cmprsk=FALSE) {
  
  # LOADING REQUIRED LIBRARIES
  require(survival)
  require(stats)
  
  #ONLY KEEPING COMPLETE CASES
  data=data[complete.cases(data[c(outcome,ttoutcome,predictors)]),c(outcome,ttoutcome,predictors)]
  
  # outcome MUST BE CODED AS 0 AND 1
  if ((length(data[!(data[outcome]==0 | data[outcome]==1),outcome])>0) & cmprsk==FALSE) {
    stop("outcome must be coded as 0 and 1")
  }

  # data MUST BE A DATA FRAME
  if (class(data)!="data.frame") {
    stop("Input data must be class data.frame")
  }
  
  # xstart IS BETWEEN 0 AND 1
  if (xstart<0 | xstart>1) {
    stop("xstart must lie between 0 and 1")
  }
  
  # xstop IS BETWEEN 0 AND 1
  if (xstop<0 | xstop>1) {
    stop("xstop must lie between 0 and 1")
  }
  
  # xby IS BETWEEN 0 AND 1
  if (xby<=0 | xby>=1) {
    stop("xby must lie between 0 and 1")
  }
  
  # xstart IS BEFORE xstop
  if (xstart>=xstop) {
    stop("xstop must be larger than xstart")
  }  
  
  #STORING THE NUMBER OF PREDICTORS SPECIFIED
  pred.n=length(predictors)
  
  #IF probability SPECIFIED ENSURING THAT EACH PREDICTOR IS INDICATED AS A T OR F
  if (length(probability)>0 & pred.n!=length(probability)) {
    stop("Number of probabilities specified must be the same as the number of predictors being checked.")
  }
  
  
  #IF harm SPECIFIED ENSURING THAT EACH PREDICTOR HAS A SPECIFIED HARM
  if (length(harm)>0 & pred.n!=length(harm)) {
    stop("Number of harms specified must be the same as the number of predictors being checked.")
  }
  
  #INITIALIZING DEFAULT VALUES FOR PROBABILITES AND HARMS IF NOT SPECIFIED
  if (length(harm)==0) {
    harm=rep(0,pred.n)
  }
  if (length(probability)==0) {
    probability=rep(TRUE,pred.n)
  }
  
  # THE PREDICTOR NAMES CANNOT BE EQUAL TO all OR none.
  if (length(predictors[predictors=="all" | predictors=="none"])) {
    stop("Prediction names cannot be equal to all or none.")
  }  

  #CHECKING THAT EACH probability ELEMENT IS EQUAL TO T OR F, 
  #AND CHECKING THAT PROBABILITIES ARE BETWEEN 0 and 1
  #IF NOT A PROB THEN CONVERTING WITH A COX REGRESSION
  for(m in 1:pred.n) { 
    if (probability[m]!=TRUE & probability[m]!=FALSE) {
      stop("Each element of probability vector must be TRUE or FALSE")
    }
    if (probability[m]==TRUE & (max(data[predictors[m]])>1 | min(data[predictors[m]])<0)) {
      stop(paste(predictors[m],"must be between 0 and 1 OR sepcified as a non-probability in the probability option",sep=" "))  
    }
    if(probability[m]==FALSE) {
      model=NULL
      pred=NULL
      model=coxph(Surv(data.matrix(data[ttoutcome]),data.matrix(data[outcome])) ~ data.matrix(data[predictors[m]]))
      surv.data=data.frame(0)
      pred=data.frame(1-c(summary(survfit(model, newdata=surv.data), time=timepoint)$surv))
      names(pred)=predictors[m]
      data=cbind(data[names(data)!=predictors[m]],pred)
      print(paste(predictors[m],"converted to a probability with Cox regression. Due to linearity and proportional hazards assumption, miscalibration may occur.",sep=" "))
    }
  }  
  
  #########  CALCULATING NET BENEFIT   #########
  N=dim(data)[1]

  # getting the probability of the event for all subjects
  # this is used for the net benefit associated with treating all patients
  if(cmprsk==FALSE) {
    km.cuminc=survfit(Surv(data.matrix(data[ttoutcome]),data.matrix(data[outcome]))~1)
    pd=1 - summary(km.cuminc, times=timepoint)$surv
  } else {
    require(cmprsk)
    cr.cuminc=cuminc(data[[ttoutcome]],data[[outcome]])
    pd=timepoints(cr.cuminc, times=timepoint)$est[1]
  }
  
  #creating dataset that is one line per threshold for the treat all and treat none strategies;
  # CREATING DATAFRAME THAT IS ONE LINE PER THRESHOLD PER all AND none STRATEGY
  nb=data.frame(seq(from=xstart, to=xstop, by=xby))
  names(nb)="threshold"
  interv=nb
  error=NULL

  nb["all"]=pd - (1-pd)*nb$threshold/(1-nb$threshold)
  nb["none"]=0
  
  # CYCLING THROUGH EACH PREDICTOR AND CALCULATING NET BENEFIT
  for(m in 1:pred.n){
    nb[predictors[m]]=NA
    
    for(t in 1:length(nb$threshold)){
      #calculating number of true and false postives;
      px=sum(data[predictors[m]]>nb$threshold[t])/N
      
      if (px==0){
        error=rbind(error,paste(predictors[m],": No observations with risk greater than ",nb$threshold[t]*100,"%",sep=""))
        break
      } else {
        #calculate risk using Kaplan Meier
        if(cmprsk==FALSE) {
          km.cuminc=survfit(Surv(data.matrix(data[data[predictors[m]]>nb$threshold[t],ttoutcome]),data.matrix(data[data[predictors[m]]>nb$threshold[t],outcome]))~1)
          pdgivenx=(1 - summary(km.cuminc, times=timepoint)$surv)
          if(length(pdgivenx)==0){
            error=rbind(error,paste(predictors[m],": No observations with risk greater than ",nb$threshold[t]*100,"% that have followup through the timepoint selected",sep=""))
            break
          }
          #calculate risk using competing risk
        }  else {
          cr.cuminc=cuminc(data[[ttoutcome]][data[[predictors[m]]]>nb$threshold[t]],data[[outcome]][data[[predictors[m]]]>nb$threshold[t]])
          pdgivenx=timepoints(cr.cuminc, times=timepoint)$est[1]
          if(is.na(pdgivenx)){
            error=rbind(error,paste(predictors[m],": No observations with risk greater than ",nb$threshold[t]*100,"% that have followup through the timepoint selected",sep=""))
            break
          }
        }
        #calculating NB based on calculated risk
        nb[t,predictors[m]]=pdgivenx*px - (1-pdgivenx)*px*nb$threshold[t]/(1-nb$threshold[t]) - harm[m]
        
      }
    }
    interv[predictors[m]]=(nb[predictors[m]] - nb["all"])*interventionper/(interv$threshold/(1-interv$threshold))
  }
  if(length(error)>0){
    print(paste(error,", and therefore net benefit not calculable in this range.",sep="")) 
  }
  
  # CYCLING THROUGH EACH PREDICTOR AND SMOOTH NET BENEFIT AND INTERVENTIONS AVOIDED 
  for(m in 1:pred.n) {
    if (smooth==TRUE){
      lws=loess(data.matrix(nb[!is.na(nb[[predictors[m]]]),predictors[m]]) ~ data.matrix(nb[!is.na(nb[[predictors[m]]]),"threshold"]),span=loess.span)
      nb[!is.na(nb[[predictors[m]]]),paste(predictors[m],"_sm",sep="")]=lws$fitted
      
      lws=loess(data.matrix(interv[!is.na(nb[[predictors[m]]]),predictors[m]]) ~ data.matrix(interv[!is.na(nb[[predictors[m]]]),"threshold"]),span=loess.span)
      interv[!is.na(nb[[predictors[m]]]),paste(predictors[m],"_sm",sep="")]=lws$fitted
    }
  }

  
  # PLOTTING GRAPH IF REQUESTED
  if (graph==TRUE) {
    require(graphics)
    
    # PLOTTING INTERVENTIONS AVOIDED IF REQUESTED
    if(intervention==TRUE) {
      # initialize the legend label, color, and width using the standard specs of the none and all lines
      legendlabel <- NULL
      legendcolor <- NULL
      legendwidth <- NULL
      legendpattern <- NULL
      
      #getting maximum number of avoided interventions
      ymax=max(interv[predictors],na.rm = TRUE)
      
      #INITIALIZING EMPTY PLOT WITH LABELS
      plot(x=nb$threshold, y=nb$all, type="n" ,xlim=c(xstart, xstop), ylim=c(ymin, ymax), xlab="Threshold probability", ylab=paste("Net reduction in interventions per",interventionper,"patients"))
      
      #PLOTTING INTERVENTIONS AVOIDED FOR EACH PREDICTOR
      for(m in 1:pred.n) {
        if (smooth==TRUE){
          lines(interv$threshold,data.matrix(interv[paste(predictors[m],"_sm",sep="")]),col=m,lty=2)     
        } else {
          lines(interv$threshold,data.matrix(interv[predictors[m]]),col=m,lty=2)
        }
        
        # adding each model to the legend
        legendlabel <- c(legendlabel, predictors[m])
        legendcolor <- c(legendcolor, m)
        legendwidth <- c(legendwidth, 1)
        legendpattern <- c(legendpattern, 2)
      }
      
    } else {
    # PLOTTING NET BENEFIT IF REQUESTED
      # initialize the legend label, color, and width using the standard specs of the none and all lines
      legendlabel <- c("None", "All")
      legendcolor <- c(17, 8)
      legendwidth <- c(2, 2)
      legendpattern <- c(1, 1)
      
      #getting maximum net benefit
      ymax=max(nb[names(nb)!="threshold"],na.rm = TRUE)
      
      # inializing new benfit plot with treat all option
      plot(x=nb$threshold, y=nb$all, type="l", col=8, lwd=2 ,xlim=c(xstart, xstop), ylim=c(ymin, ymax), xlab="Threshold probability", ylab="Net benefit")
      # adding treat none option
      lines(x=nb$threshold, y=nb$none,lwd=2)
      #PLOTTING net benefit FOR EACH PREDICTOR
      for(m in 1:pred.n) {
        if (smooth==TRUE){
          lines(nb$threshold,data.matrix(nb[paste(predictors[m],"_sm",sep="")]),col=m,lty=2) 
        } else {
          lines(nb$threshold,data.matrix(nb[predictors[m]]),col=m,lty=2)
        }
        # adding each model to the legend
        legendlabel <- c(legendlabel, predictors[m])
        legendcolor <- c(legendcolor, m)
        legendwidth <- c(legendwidth, 1)
        legendpattern <- c(legendpattern, 2)
      }
    }
    # then add the legend
    legend("topright", legendlabel, cex=0.8, col=legendcolor, lwd=legendwidth, lty=legendpattern)
    
  }
  
  #RETURNING RESULTS
  results=list() 
  results$N=N
  results$predictors=data.frame(cbind(predictors,harm,probability))
  names(results$predictors)=c("predictor","harm.applied","probability")
  results$interventions.avoided.per=interventionper
  results$net.benefit=nb
  results$interventions.avoided=interv
  return(results)
  
}