R/cox_boost_fun.R

In SydneyBioX/SurvBenchmark_package: survival approaches benchmarking

#' CoxBoost model
#'
#'
#' @param r a numeric value, a seed to run this method
#' @param data a dataframe, the data used to performance this survival model
#' @param cvK a numeric value, cross-validation fold
#' @param fitform_ogl a Surv object from package survival, the survival function
#' @param formula1 a Surv object from package survival, to caulculate a version of the brier score, details please check package pec
#' @param formula2 a Surv object from package survival, to caulculate a version of the brier score, details please check package pec
#' @param formula3 a Surv object from package survival, to caulculate a version of the brier score, details please check package pec
#' @param formula4 a Surv object from package survival, to caulculate a version of the brier score, details please check package pec
#' @param timess a numeric vector of length 15, contains time points to get the time-dependent AUC values
#' @param time1 a numeric value, the time point to calculate the risk, see package "CoxBoost"
#' @param stepnumber a numeric value, the number of stpes performed in the model, see package "CoxBoost"
#' @param penaltynumber a numeric value, the penalty number used in the model, see package "CoxBoost"
#' @return a data.frame with allevaluation measurements in all columns and rows are each fold results from cross-validation
#'
#' @examples
#' data("exampledt", package = "SurvBenchmark")
#' fitform_ogl=survival::Surv(time,status)~.
#' formula1=fitform_ogl
#' formula2=fitform_ogl
#' formula3=survival::Surv(time,status)~1
#' formula4=survival::Surv(time,status)~1
#' form1=as.formula(~.)
#' timess=seq(as.numeric(summary(cancerdt2_1$time)[2]),as.numeric(summary(cancerdt2_1$time)[5]),(as.numeric(summary(cancerdt2_1$time)[5])-as.numeric(summary(cancerdt2_1$time)[2]))/14)

#' time1=timess[3]
#' stepnumber=10
#' penaltynumber=100
#' want=cox_boost_fun(1,cancerdt2_1[,-dim(cancerdt2_1)[2]],5,fitform_ogl,formula1,formula2,formula3,formula4,time1,timess,stepnumber,penaltynumber);
#' @export



cox_boost_fun=function(r,data,cvK,fitform_ogl,formula1,formula2,formula3, formula4, time1,timess,stepnumber,penaltynumber){
  if (! is.numeric(r)) stop("Input seed is wrong")
  if (! is.numeric(cvK)) stop("Input cross-validation fold number is wrong")
  if (! is.numeric(time1)) stop("Input time point is wrong")
  if (! is.numeric(stepnumber)) stop("Input stepnumber is wrong")
  if (! is.numeric(penaltynumber)) stop("Input penaltynumber is wrong")
  if (is.null(dim(data))) stop("Input data is wrong")
  if (length(timess)!=15) stop("Wrong time vector length")
  if (class(timess)!= "numeric") stop("Wrong time vector type")

  set.seed(r)
  print(r)
  cvSets = cvTools::cvFolds(nrow(data), cvK)  # permute all the data, into 5 folds
  bicfun=purrr::possibly(function(j){
    test_id = cvSets$subsets[cvSets$which == j]
    test = data[test_id, ]
    train = data[-test_id, ]

    tr_predictormatrix=train[,-which(colnames(train)%in% c("time","status"))]
    tr_predictormatrix=data.matrix(tr_predictormatrix)
    te_predictormatrix=test[,-which(colnames(test)%in% c("time","status"))]
    te_predictormatrix=data.matrix(te_predictormatrix)
    coxboost=CoxBoost::CoxBoost(time=train$time,status=train$status,x=tr_predictormatrix,stepno=stepnumber,penalty=penaltynumber)
    lpnew=predict(coxboost,type="risk",times=time1,newdata=te_predictormatrix)
    #it is the survival probability #the predicted probability of not yet having had the event at the time points given in times
    lpnew=-lpnew #change to risk

    lp<- predict(coxboost,type="risk",times=time1,newdata=tr_predictormatrix)
    lp=-lp
    #cindex
    harrelC1 <- Hmisc::rcorr.cens(-lpnew,survival::Surv(test$time,test$status))
    hc_1<-harrelC1["C Index"]
    Surv.rsp <- survival::Surv(train$time, train$status)
    Surv.rsp.new <- survival::Surv(test$time, test$status)
    bc_1 <- survAUC::BeggC(Surv.rsp, Surv.rsp.new,lp, lpnew)
    unoc_1<-survAUC::UnoC(Surv.rsp, Surv.rsp.new, lpnew)
    ghc_1<-survAUC::GHCI(lpnew)


    #br
    briers1 <- survAUC::predErr(Surv.rsp, Surv.rsp.new, lp, lpnew,times=test$time, type = "brier", int.type = "unweighted")$error
    br1<-sum(na.omit(briers1))
    briers2<-survAUC::predErr(Surv.rsp, Surv.rsp.new, lp, lpnew,times=test$time, type = "brier", int.type = "weighted")$error
    br2<-sum(na.omit(briers2))
    ibsfun1=purrr::possibly(function(modell){
      briers3 <- pec::pec(list("cox1"=modell),data=test,formula=fitform_ogl1,cens.model="cox")
      return(crps(briers3)[2])
    },otherwise = NA)
    br3<-NA
    ibsfun2=purrr::possibly(function(modell){
      briers4 <- pec::pec(list("cox1"=modell),data=test,formula=fitform_ogl2,cens.model="marginal")
      return(crps(briers4)[2])
    },otherwise = NA)
    br4<-NA
    ibsfun3=purrr::possibly(function(modell){
      briers5 <- pec::pec(list("cox1"=modell),data=test,formula=fitform_ogl3,cens.model="cox")
      return(crps(briers5)[2])
    },otherwise = NA)
    br5<-NA
    ibsfun4=purrr::possibly(function(modell){
      briers6 <- pec::pec(list("cox1"=modell),data=test,formula=fitform_ogl4,cens.model="marginal")
      return(crps(briers6)[2])
    },otherwise = NA)
    br6<-NA
    #time-dependent auc
    times <- timess
    AUC_CD <- survAUC::AUC.uno(Surv.rsp, Surv.rsp.new, lpnew, times)
    a1=AUC_CD$auc[1]
    a2=AUC_CD$auc[2]
    a3=AUC_CD$auc[3]
    a4=AUC_CD$auc[4]
    a5=AUC_CD$auc[5]
    a6=AUC_CD$auc[6]
    a7=AUC_CD$auc[7]
    a8=AUC_CD$auc[8]
    a9=AUC_CD$auc[9]
    a10=AUC_CD$auc[10]
    a11=AUC_CD$auc[11]
    a12=AUC_CD$auc[12]
    a13=AUC_CD$auc[13]
    a14=AUC_CD$auc[14]
    a15=AUC_CD$auc[15]
    a=AUC_CD$iauc
    return(c(hc_1,bc_1,unoc_1,ghc_1,br1,br2,br3,br4,br5,br6,a1,a2,a3,a4,a5,a6,a7,a8,a9,a10,a11,a12,a13,a14,a15,a))},otherwise=NA)

  cv5_result=rbind.data.frame(bicfun(1),bicfun(2),bicfun(3),bicfun(4),bicfun(5))
  #colnames(cv5_result)=c("hc_1","bc_1","unoc_1","ghc_1","br1","br2","br3","br4","br5","br6","a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12","a13","a14","a15","a")


  return(cv5_result)}