WPC (version 1.0)

SoloWPCCurve: Generate Single Weighted Predictiveness Curve in Graph

Description

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.

Usage

SoloWPCCurve(wpc, xlab, ylab, main, ylim , xlim, type, col, lwd, legendloc, 
legendtxt, confi, ptsest)

Arguments

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.

References

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

See Also

DuoWPCCurve, TrioWPCCurve

Examples

	# 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")