Description Usage Arguments Author(s) References See Also Examples
This function will generate one single weighted predictiveness curve in graph using the estimates provided by "npr.wpc.est" function. It helps to visualize the relationship between survival rate and biomarker.
1 2 | SoloWPCCurve(wpc, xlab, ylab, main, ylim , xlim, type, col, lwd, legendloc,
legendtxt, confi, ptsest)
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wpc |
It is the object generated by function cox.wpc.est or npr.wpc.est. |
xlab |
It is the title for x axis; default is "Marker". |
ylab |
It is the title for y axis; default is "Survival Rate". |
main |
It is the title for the plot; default is "Weighted Predictiveness Curve". |
ylim |
It creates the continuous scale of y axis of the plot; default is "c(0,1)". |
xlim |
It creates the continuous scale of y axis of the plot; default is "c(0,100)". |
type |
It defines the type of the curve; default is "l". |
col |
It defines the color of the curve; default is "red". |
lwd |
It defines the width of the curve; default is "2". |
legendloc |
It specifies the location of the legend; default is "bottomright". |
legendtxt |
It provides the text of the legend; defalut is "c("Method1")". |
confi |
It provides the option of drawing the confidence bands; default is "N", which means no confidence band is needed; "Y" will report the confidence band. |
ptsest |
It provides the option of drawing the point estimates; default is "N", which means no point estimates is needed; "Y" will report the point estimates. |
Hui Yang huiy@amgen.com, Rui Tang rui_tang@vrtx.com and Jing Huang jinghuang0@gmail.com
Yang H., Tang R., Hale M. and Huang J. (2016) A visualization method measuring the performance of biomarkers for guiding treatment decisions Pharmaceutical Statistics, 15(2), 1539-1612
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # Get the estimate of predictiveness curve from npr.wpc.est functions
# and print the corresponding predictiveness curve
npr.object = npr.wpc.est(event=wpcdata$OSday, censor=wpcdata$OScensor,
marker=wpcdata$Biomarker1,cutoff=180,method="number.subjt",weights="normal",
nsub=10,sspeed=1,df=2,confi="NO")
SoloWPCCurve(npr.object,xlab="Marker",ylab="Survival Rate",
main="Weighted Predictiveness Curve",ylim=c(0,1),xlim=c(0,100),
type="l",col="red",lwd=2,confi="N",ptsest="Y")
# Get the estimate of predictiveness curve from cox.wpc.est functions
# and print the corresponding predictiveness curve
cox.object = cox.wpc.est(event=wpcdata$OSday, censor=wpcdata$OScensor,
marker=wpcdata$Biomarker1,cutoff=180,quantile=0.95)
SoloWPCCurve(cox.object,xlab="Marker",ylab="Survival Rate",
main="Weighted Predictiveness Curve",ylim=c(0,1),xlim=c(0,100),
type="l",col="red",lwd=2,confi="N",ptsest="Y")
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