程序包准备，#一定要把dca.R放在之前设定的起始目录中。
source("dca.R")#需要把这个代码放到加载目录里，文章最后会贴出，用的时候直接复查到.R后缀的文件并改名为dca.R就行
library(nricens)
library(rms)
library(foreign)
#数据准备
mydata<-read.table("2020090873.csv",header=T,sep = ",")#读取当前目录文件，CSV文件分割为","，标题为真，第一行设定为标题
str(mydata)#展示数据集的结构
mydata$MVI<-factor(mydata$MVI,labels=c("M1","M2","M3"))
mydata$preHBeAg<-factor(mydata$preHBeAg,labels=c("negative","positive"))
mydata$diameter5<-factor(mydata$diameter5,labels=c("≤5","＞5"))
mydata$Multiple.shots<-factor(mydata$Multiple.shots,labels=c("Single","Multiple"))
mydata$Satellite<-factor(mydata$Satellite,labels=c("no","yes"))
mydata$Serosa<-factor(mydata$Serosa,labels=c("no","yes"))
#把需要应用的分类变量设定哑变量并设定为无序多分类变量，原则上二分类变量无序上述改变，但改变后后续列线图会同时显示lable的名称。
#AFP值取了自然对数为底的对数，对其进行正太化，此处未显示
str(mydata)#再次展示数据集的结构，注意确定为因子变量的变量名称变化
attach(mydata)#把数据集加载入当前环境中
dev = mydata[mydata$devlopval==0,]#根据devlopval拆分为建模集
vad = mydata[mydata$devlopval==1,]#根据devlopval拆分为验证集
#构建三个回归模型
modelA <- glm(team ~ LnAFP+preHBeAg +diameter5+Multiple.shots+Serosa+MVI+Satellit, data = dev, family = binomial(link="logit"),x=TRUE)
summary(modelA)
cbind(coef= coef(modelA),confint(modelA))
exp(cbind(OR= coef(modelA),confint(modelA)))
dev$predmodelA<- predict(newdata=dev,modelA,"response")
modelB <- glm(team ~BCLC , data = dev, family = binomial(link="logit"),x=TRUE)
summary(modelB)
cbind(coef= coef(modelB),confint(modelB))
exp(cbind(OR= coef(modelB),confint(modelB)))
dev$predmodelB<- predict(newdata=dev,modelB,"response")
modelC <- glm(team ~AJCC8th , data = dev, family = binomial(link="logit"),x=TRUE)
summary(modelC)
cbind(coef= coef(modelC),confint(modelC))
exp(cbind(OR= coef(modelC),confint(modelC)))
dev$predmodelC<- predict(newdata=dev,modelC,"response")
#建模人群Decision Curve Analysis
dca(data=dev, outcome="team", predictors=c("predmodelA", "predmodelB","predmodelC"),smooth="TRUE", probability=c("TRUE", "TRUE","TRUE"))
#验证人群Decision Curve Analysis
#计算验证人群预测值
vad$predmodelA<- predict(newdata=vad,modelA,"response")
vad$predmodelB<- predict(newdata=vad,modelB,"response")
vad$predmodelC<- predict(newdata=vad,modelC,"response")
dca(data=vad, outcome="team", predictors=c("predmodelA", "predmodelB","predmodelC"),smooth="TRUE", probability=c("TRUE", "TRUE","TRUE"))
验证组DCA曲线
以下为dca.R的内容
dca <- function(data, outcome, 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) {
# LOADING REQUIRED LIBRARIES
require(stats)
# data MUST BE A DATA FRAME
if (class(data)!="data.frame") {
stop("Input data must be class data.frame")
}
#ONLY KEEPING COMPLETE CASES
data=data[complete.cases(data[append(outcome,predictors)]),append(outcome,predictors)]
# outcome MUST BE CODED AS 0 AND 1
if (max(data[[outcome]])>1 | min(data[[outcome]])<0) {
stop("outcome cannot be less than 0 or greater than 1")
}
# 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 YES OR NO
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)
}
#CHECKING THAT EACH probability ELEMENT IS EQUAL TO YES OR NO,
#AND CHECKING THAT PROBABILITIES ARE BETWEEN 0 and 1
#IF NOT A PROB THEN CONVERTING WITH A LOGISTIC 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=glm(data.matrix(data[outcome]) ~ data.matrix(data[predictors[m]]), family=binomial("logit"))
pred=data.frame(model$fitted.values)
pred=data.frame(pred)
names(pred)=predictors[m]
data=cbind(data[names(data)!=predictors[m]],pred)
print(paste(predictors[m],"converted to a probability with logistic regression. Due to linearity assumption, miscalibration may occur.",sep=" "))
}
}
# THE PREDICTOR NAMES CANNOT BE EQUAL TO all OR none.
if (length(predictors[predictors=="all" | predictors=="none"])) {
stop("Prediction names cannot be equal to all or none.")
}
######### CALCULATING NET BENEFIT #########
N=dim(data)[1]
event.rate=colMeans(data[outcome])
# CREATING DATAFRAME THAT IS ONE LINE PER THRESHOLD PER all AND none STRATEGY
nb=data.frame(seq(from=xstart, to=xstop, by=xby))
names(nb)="threshold"
interv=nb
nb["all"]=event.rate - (1-event.rate)*nb$threshold/(1-nb$threshold)
nb["none"]=0
# CYCLING THROUGH EACH PREDICTOR AND CALCULATING NET BENEFIT
for(m in 1:pred.n){
for(t in 1:length(nb$threshold)){
# COUNTING TRUE POSITIVES AT EACH THRESHOLD
tp=mean(data[data[[predictors[m]]]>=nb$threshold[t],outcome])*sum(data[[predictors[m]]]>=nb$threshold[t])
# COUNTING FALSE POSITIVES AT EACH THRESHOLD
fp=(1-mean(data[data[[predictors[m]]]>=nb$threshold[t],outcome]))*sum(data[[predictors[m]]]>=nb$threshold[t])
#setting TP and FP to 0 if no observations meet threshold prob.
if (sum(data[[predictors[m]]]>=nb$threshold[t])==0) {
tp=0
fp=0
}
# CALCULATING NET BENEFIT
nb[t,predictors[m]]=tp/N - fp/N*(nb$threshold[t]/(1-nb$threshold[t])) - harm[m]
}
interv[predictors[m]]=(nb[predictors[m]] - nb["all"])*interventionper/(interv$threshold/(1-interv$threshold))
}
# CYCLING THROUGH EACH PREDICTOR AND SMOOTH NET BENEFIT AND INTERVENTIONS AVOIDED
for(m in 1:pred.n) {
if (smooth==TRUE){
lws=loess(data.matrix(nb[!is.na(nb[[predictors[m]]]),predictors[m]]) ~ data.matrix(nb[!is.na(nb[[predictors[m]]]),"threshold"]),span=loess.span)
nb[!is.na(nb[[predictors[m]]]),paste(predictors[m],"_sm",sep="")]=lws$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)
}
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