boxplot.Score: Boxplot risk quantiles

In riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

Boxplot risk quantiles

Description

Retrospective boxplots of risk quantiles conditional on outcome

Usage

## S3 method for class 'Score'
boxplot(
  x,
  model,
  reference,
  type = "risk",
  timepoint,
  overall = 1L,
  lwd = 3,
  xlim,
  xlab = "",
  main,
  outcome.label,
  outcome.label.offset = 0,
  event.labels,
  refline = (type != "risk"),
  add = FALSE,
  ...
)

Arguments

x	Score object obtained by calling function Score.
model	Choice of risk prediction model
reference	Choice of reference risk prediction model for calculation of risk differences.
type	Either "risk" for predicted risks or "diff" for differences between predicted risks.
timepoint	time point specifying the prediction horizon
overall	Logical. Tag to be documented.
lwd	line width
xlim	x-axis limits
xlab	x-axis label
main	title of plot
outcome.label	Title label for column which shows the outcome status
outcome.label.offset	Vertical offset for outcome.label
event.labels	Labels for the different events (causes).
refline	Logical, for type="diff" only. If TRUE draw a red vertical line at 0.
add	Logical. Tag to be documented.
...	not used

Examples

# binary outcome
library(data.table)
library(prodlim)
set.seed(10)
db=sampleData(40,outcome="binary")
fitconv=glm(Y~X3+X5,data=db,family=binomial)
fitnew=glm(Y~X1+X3+X5+X6+X7,data=db,family=binomial)
x=Score(list(new=fitnew,conv=fitconv),
        formula=Y~1,contrasts=list(c(2,1)),
               data=db,plots="box",null.model=FALSE)
boxplot(x)

# survival outcome
library(survival)
ds=sampleData(40,outcome="survival")
fit=coxph(Surv(time,event)~X6+X9,data=ds,x=TRUE,y=TRUE)
## Not run:  
scoreobj=Score(list("Cox"=fit),
                formula=Hist(time,event)~1, data=ds,
                metrics=NULL, plots="box",
                times=c(1,5),null.model=FALSE)
boxplot(scoreobj,timepoint=5)
boxplot(scoreobj,timepoint=1)


## End(Not run)

# competing risks outcome
library(survival)
data(Melanoma, package = "riskRegression")
fit = CSC(Hist(time,event,cens.code="censored")~invasion+age+sex,data=Melanoma)
scoreobj=Score(list("CSC"=fit),
               formula=Hist(time,event,cens.code="censored")~1,
               data=Melanoma,plots="box",times=5*365.25,null.model=FALSE)
par(mar=c(4,12,4,4))
boxplot(scoreobj,timepoint=5*365.25)

# more than 2 competing risks
m=lava::lvm(~X1+X2+X3)
lava::distribution(m, "eventtime1") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "eventtime2") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "eventtime3") <- lava::coxWeibull.lvm(scale = 1/100)
lava::distribution(m, "censtime") <- lava::coxWeibull.lvm(scale = 1/100)
lava::regression(m,eventtime2~X3)=1.3
m <- lava::eventTime(m,
time ~ min(eventtime1 = 1, eventtime2 = 2, eventtime3 = 3, censtime = 0), "event")
set.seed(101)
dcr=as.data.table(lava::sim(m,101))
fit = CSC(Hist(time,event)~X1+X2+X3,data=dcr)
scoreobj=Score(list("my model"=fit),
               formula=Hist(time,event)~1,
               data=dcr,plots="box",times=5,null.model=FALSE)
boxplot(scoreobj)

riskRegression documentation built on Sept. 8, 2023, 6:12 p.m.