In tagteam/MedicalRiskPredictionModels: R-codes for book: Medical Risk Prediction Models by Gerds & kattan

library(MedicalRiskPredictionModels)
prepareExamples() 

# Chunk1
fit <- glm(ohss~ant.foll+cyclelen+age+smoking,data=ivftrain,family="binomial")
x <- Score(list(fit),formula=ohss~1,data=ivftest,summary="riskQuantiles")
boxplot(x)
abline(v=mean(ivftest$ohss),lty=2)
abline(v=mean(predictRisk(fit,newdata=ivftest)),lty=3) 

# Chunk2
fit <- glm(ohss~ant.foll+cyclelen+age+smoking,data=ivftrain,family="binomial")
Score(list("My model"=fit),formula=ohss~1,data=ivftest,metrics="brier",summary="ipa") 

# Chunk3
fit <- glm(ohss~ant.foll+cyclelen+age+smoking,data=ivftrain,family="binomial")
Score(list("My model"=fit),formula=ohss~1,data=ivftest,metrics="auc") 

# Chunk4
fit <- glm(ohss~ant.foll+smoking+age,data=ivftrain,family=binomial)
x <- Score(list("My-model"=fit), data=ivftest, formula=ohss~1,
       plots="ROC")
plotROC(x) 

# Chunk5
fit <- glm(ohss~ant.foll+smoking+age,data=ivftrain,family=binomial)
x <- Score(list("My-model"=fit), data=ivftest, formula=ohss~1,
        plots="calibration")
plotCalibration(x,bars=1,q=10) 

# Chunk6
plotCalibration(x,bars=1,q=3) 

# Chunk7
layout(matrix(c(1,2),nrow=2),height=c(.7,0.3))
plotCalibration(x)
boxplot(x$Calibration$plotframe$risk,horizontal=TRUE, 
        main="",xlab="",axes=FALSE,ylim=c(0,1)) 

# Chunk8
fit <- coxph(Surv(survtime,survstatus)~age+tumorthickness+grade,data=octrain,x=1)
x <- Score(list("My-model"=fit),
       formula=Surv(survtime,survstatus)~1, data=octest,
       times=120, summary=c("riskQuantiles"), null.model=0)
boxplot(x,event.labels=c("Overall","Dead","Alive"),outcome.label="10-year\nmortality") 

# Chunk9
# Kaplan-Meier:
F <- prodlim(Hist(survtime,survstatus)~1,data=octest)
plot(F)
# reverse Kaplan-Meier
G <- prodlim(Hist(survtime,survstatus)~1,data=octest,reverse=1)
plot(G) 

# Chunk10
fit <- coxph(Surv(survtime,survstatus)~age+tumorthickness+grade,data=octrain,x=1)
x <- Score(list("Cox"=fit),
       data=octest,
       formula=Surv(survtime,survstatus)~1,
       times=120,
       metrics="brier")
x 

# Chunk11
fit <- coxph(Surv(survtime,survstatus)~age+tumorthickness+grade,data=octrain,x=1)
x <- Score(list("Cox regression"=fit),
       data=octest,
       formula=Surv(survtime,survstatus)~1,
       times=120,
       metrics="auc")
x 

# Chunk12
fit <- coxph(Surv(survtime,survstatus)~age+tumorthickness+grade,data=octrain,x=1L)
x <- Score(list("Cox"=fit),data=octest,
       formula=Surv(survtime,survstatus)~1,
       times=120,plots="ROC")
plotROC(x, plot.main="Outcome: 10 year all-cause mortality",auc=1) 

# Chunk13
fit <- coxph(Surv(survtime,survstatus)~age+tumorthickness+grade,data=octrain,x=1L)
x <- Score(list("Cox"=fit), data=octest,
       formula=Surv(survtime,survstatus)~1,
       times=120, plots="calibration")
layout(matrix(c(1,2),nrow=2),height=c(.7,0.3))
plotCalibration(x, pseudo=0, rug=1, cens.method="local",
    plot.main="Outcome: 10 year all-cause mortality")
boxplot(x$Calibration$plotframe$risk,horizontal=TRUE,
        main="",xlab="",axes=FALSE,ylim=c(0,1)) 

# Chunk14
fit <- CSC(list(Hist(asprogtime,asprog)~psa+ct1+diaggs,
        Hist(asprogtime,asprog)~age),
       data=astrain,cause="progression")
Score(list("CSC"=fit),
      data=astest,
      formula=Hist(asprogtime,asprog)~1,
      times=3,
      metrics="brier",
      cause="progression") 

# Chunk15
fit <- CSC(list(Hist(asprogtime,asprog)~psa+ct1+diaggs,
        Hist(asprogtime,asprog)~age),
       data=astrain,cause="progression")
Score(list("CSC"=fit),data=astest,formula=Hist(asprogtime,asprog)~1,times=3,metrics="auc",cause="progression") 

# Chunk16
fit <- CSC(list(Hist(asprogtime,asprog)~psa+ct1+diaggs,
        Hist(asprogtime,asprog)~age),
       data=astrain,cause="progression")
x <- Score(list("CSC"=fit),
           data=astest,
           formula=Hist(asprogtime,asprog)~1,
           times=3,
           metrics="auc",
           plots="ROC",
           cause="progression")
plotROC(x,plot.main="Outcome: 3 year cancer progression") 

# Chunk17
fit <- CSC(list(Hist(asprogtime,asprog)~psa+ct1+diaggs,
        Hist(asprogtime,asprog)~age),
       data=astrain,cause="progression")
x <- Score(list("CSC"=fit), data=astest,
       formula=Hist(asprogtime,asprog)~1, times=3,
       plots="calibration", cause="progression")
plotCalibration(x,cens.method="local") 

r # Chunk18 fit <- CSC(list(Hist(asprogtime,asprog)~psa+ct1+diaggs, Hist(asprogtime,asprog)~age), data=astrain,cause="progression") x <- Score(list("CSC"=fit),cause="progression",contrasts=FALSE, data=astest, formula=Hist(asprogtime,asprog)~1, times=3, summary="ipa",metrics="brier") x

# Chunk19
fit <- coxph(Surv(survtime,survstatus)~age+tumorthickness+grade,
         data=octrain, x=1L)
x <- Score(list("Cox"=fit),
       data=octest, formula=Surv(survtime,survstatus)~1,
       times=seq(12,120,12), se.fit=0, contrasts=FALSE,
       summary="ipa", contrast=FALSE) 

tagteam/MedicalRiskPredictionModels documentation built on Dec. 23, 2020, 9:48 a.m.