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

library(MedicalRiskPredictionModels)
prepareExamples() 

# Chunk1
fit <- glm(ohss~ cyclelen + bmi + weight + age + ant.foll + fsh + smoking + no.cig.d + ovolume,data=ivftrain,family="binomial") 

# Chunk2
fit <- glm(ohss~ cyclelen + bmi + weight + age + ant.foll + fsh + no.cig.d + ovolume,data=ivftrain,family="binomial")
fit 

# Chunk3
fit <- glm(ohss~ cyclelen + bmi + weight + age + ant.foll + fsh + smoking + ovolume,data=ivftrain,family="binomial")
fit 

# Chunk4
library(FactoMineR)
Vars <- ivftrain[,c("cyclelen","bmi","weight","age","ant.foll","fsh","ovolume")]
fit <- PCA(Vars,scale.unit=TRUE,ncp=5,graph=FALSE)
plot(fit,new.plot=FALSE,choix="var") 

# Chunk5
fit <- lrm(ohss~ smoking+ rcs(cyclelen,3) + rcs(bmi,3) + rcs(weight,3) + rcs(age,3) + rcs(ant.foll,3) + rcs(fsh,3) + rcs(ovolume,3),data=ivftrain)
coef(fit) 

# Chunk6
fit <- lrm(ohss~ smoking+ rcs(cyclelen,3) + rcs(bmi,3) + rcs(weight,3) + rcs(age,3) + rcs(ant.foll,3) + rcs(fsh,3) + rcs(ovolume,3),
            data=ivftrain,
            penalty=10)
coef(fit) 

# Chunk7
fit1 <- lrm(ohss~ smoking+ rcs(cyclelen,3) + rcs(bmi,3) + rcs(weight,3) + rcs(age,3) + rcs(ant.foll,3) + rcs(fsh,3) + rcs(ovolume,3),
            data=ivftrain,penalty=0)
fit2 <- lrm(ohss~ smoking+ rcs(cyclelen,3) + rcs(bmi,3) + rcs(weight,3) + rcs(age,3) + rcs(ant.foll,3) + rcs(fsh,3) + rcs(ovolume,3),
            data=ivftrain,
            penalty=1)
x <- Score(list(unpenalized=fit1,penalized=fit2),data=ivftest,formula=ohss~1)
summary(x,what="score") 

# Chunk8
fit <- glm(ohss~ cyclelen + bmi + weight + age + ant.foll + fsh + smoking + ovolume,data=ivftrain,family="binomial")
publish(fit) 

# Chunk9
library(penalized)
form <- Surv(survtime,survstatus)~ age + tumorthickness + genderMale + tobaccoNever + deep.invasionYes + siteFloor.of.Mouth + siteHard.Palate + siteLower.Gum + siteRetromolar.Trigone + siteTongue + siteUpper.Gum + raceNonCauc + x.posnodes + tumormaxdimension + vascular.invasionYes
# Elastic net
fit.elnet <- penalized(form, data=octrain.dummy, model="cox",
                       lambda1=1.5, lambda2=2.5) # L1 and L2 penalty 
# LASSO
fit.lasso <- penalized(form,data=octrain.dummy,model="cox",
                       lambda1=4,lambda2=0) # no L2 penalty 
# Ridge regression
fit.ridge <- penalized(form,data=octrain.dummy,model="cox",
                       lambda1=0,lambda2=3) # no L1 penalty  

# Chunk10
# sequence of candidate penalty values 
penalities <- c(0.01,10,100,1000)
# sequence of fitted LASSO models
fit.penal <- lapply(penalities,function(pen){
  fit <- penalizedS3(form,data=octrain.dummy,model="cox",lambda2=0,lambda1=pen,
                     trace=FALSE)
  fit$call$lambda1 <- eval(pen)
  fit
})
# cross-validated Brier score and AUC
x.steps <- Score(fit.penal, 
                 formula=Surv(survtime,survstatus)~1,
                 seed=9, # random seed
                 data=octrain.dummy, # training data
                 times=60, # prediction time horizon
                 split.method="cv5",# 5-fold cross-validation
                 B=5) # repeated 5 times 

# Chunk11
library(randomForest) # Liaw & Wiener
fit.rf <- randomForest(OHSS~age+ant.foll,data=ivftrain,n.tree=1000)
library(randomForestSRC) # Ishwaran & Kogalur
fit.rfsrc <- rfsrc(OHSS~age+ant.foll,data=ivftrain,n.tree=1000) 

# Chunk12
set.seed(98)
fit1 <- rfsrc(OHSS~age+ant.foll,data=ivftrain,n.tree=1000)
set.seed(98)
fit2 <- randomForest(OHSS~age+ant.foll,data=ivftrain,ntree=1000)
x <- Score(list("Ishwaran & Kogalur"=fit1,"Liaw & Wiener"=fit2),
           data=ivftest,formula=OHSS~1,summary="ipa")
summary(x,what="score") 

# Chunk13
set.seed(98)
fit3 <- ranger(OHSS~age+ant.foll,data=ivftrain,num.tree=1000,
               probability=1)
x <- Score(list("Wright & Ziegeler"=fit3),
           data=ivftest,formula=OHSS~1,summary="ipa")
summary(x,what="score") 

# Chunk14
set.seed(98)
fit1 <- rfsrc(OHSS~age+ant.foll,data=ivftrain,n.tree=1000,nodesize=28)
set.seed(98)
fit2 <- randomForest(OHSS~age+ant.foll,data=ivftrain,n.tree=1000,nodesize=28)
set.seed(98)
fit3 <- ranger(OHSS~age+ant.foll,data=ivftrain,
               num.tree=1000,min.node.size =28,probability=1)
x <- Score(list("Ishwaran & Kogalur"=fit1,"Liaw & Wiener"=fit2,"Wright & Ziegeler"=fit3),
           data=ivftest,formula=OHSS~1,summary="ipa")
summary(x,what="score") 

# Chunk15
set.seed(1) # the model depends on the random seed
nn <- neuralnet(ohss ~ age + ant.foll,
                data=ivftrain,
                hidden=5, # tuning parameter 
                act.fct = "logistic") # outcome activation function 

# Chunk16
fit1 <- cph(Surv(survtime,survstatus)~rcs(age,3)+tumorthickness+gender+tobacco+deep.invasion+site+race+x.posnodes+tumormaxdimension+vascular.invasion,
            data=octrain.cc, x=TRUE, y=TRUE, surv=TRUE)
set.seed(1972)
fit2 <- rfsrc(Surv(survtime,survstatus)~ age+tumorthickness+gender+tobacco+deep.invasion+site+race+x.posnodes+tumormaxdimension+vascular.invasion,data=octrain.cc)
x <- Score(list("Cox"=fit1,"Forest"=fit2),
           data=octest.cc,
           formula=Surv(survtime,survstatus)~1,
           times=120,
           summary=c("risks"))
level1.data <- dcast(ID ~model,value.var="risk",data=x$risks$score)
level1.data 

# Chunk17
library(SuperLearner)
set.seed(17) # the result depends on the random seed
fit <- SuperLearner(Y=ivftrain[["ohss"]],X=ivftrain[,c("age","ant.foll")],SL.library=c("SL.glm","SL.randomForest","SL.nnet"),family="binomial")
fit$coef 

# Chunk18
fit1 <- glm(OHSS~age+ant.foll,data=ivftrain,family="binomial")
set.seed(2)
fit2 <- randomForest(OHSS~age+ant.foll,data=ivftrain,ntree=1000,importance=0)
set.seed(3)
fit3 <- predict(nnet(OHSS~age+ant.foll,data=ivftrain,size=2),newdata=ivftest)
set.seed(4)
SL.fit <- SuperLearner(Y=ivftrain[["ohss"]],
                       X=ivftrain[,c("age","ant.foll")],
                       SL.library=c("SL.glm","SL.randomForest","SL.nnet"),
                       family="binomial")
fit4 <- predict(SL.fit,newdata=ivftest[,.(age,ant.foll)])$pred
x <- Score(list("Logistic regression"=fit1,"Random Forest"=fit2,"Neural net"=fit3,"super learner"=fit4),
           data=ivftest,formula=OHSS~1,summary="ipa")
summary(x,what="score") 

# Chunk19
p <- predict(fit,newdata=data.frame(age=25,ant.foll=17))
p 

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