WPC (version 1.0)

DuoScattorPlot: Generate Scatter Plots for Time-to-Event and Biomarkers for Two Groups

Description

This function will generate the scatter plot of time-to-event and biomarker for two dataset. It helps to visualize the relationship between survival endpoints and biomarkers. It can also help to compare the two datasets

Usage

DuoScattorPlot(data1, data2, cutoff, xlab, ylab, main, ylim, xlim, col1, col2, col3, lwd, 
pch1, pch2, legendloc, legendtxt, ncol)

Arguments

data1

Data object 1 with three variables included: event: the survival time, a positive numerical vector with no missing values; censor: the censor information, a vector with 1 indicating an event and 0 indicating right censored; marker: the biomarker information, or other interesting variables.

data2

Data object 2 with the same structure as data object 1.

cutoff

This is to define the interesting data cutoff time point to see the relationship between time-to-events and markers.

xlab

It is the title for x axis; default is "Marker".

ylab

It is the title for y axis; default is "Time to Event".

main

It is the title for the plot; default is "Scattor Plot".

ylim

It creates the continuous scale of y axis of the plot; default is "c(0,3600)".

xlim

It creates the continuous scale of y axis of the plot; default is "c(0,100)".

col1

It defines the color of the dot in the dataset 1; default is "red".

col2

It defines the color of the dot in the dataset 2; default is "black".

col3

It defines the color of the cutoff line; default is "tomato".

lwd

It defines the width of the cutoff line; default is "2".

pch1

It defines the type of the dot for event; default is "20".

pch2

It defines the type of the dot for censor; default is "21".

legendloc

It specifies the location of the legend; default is "bottomright".

legendtxt

It provides the text of the legend; default is "c("death-group1","censor-group1","death-group2","censor-group2")".

ncol

It specifies the number of columns displayed in legend; default=1

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

SoloScattorPlot, TrioScattorPlot

Examples

	## Create two data objects for the function:
	
        
	tmppb = wpcdata[wpcdata$ATRT=="Placebo",]
	tmptrt = wpcdata[wpcdata$ATRT=="Treatment",]
	o.data1 =data.frame(event=tmppb$OSday, censor=tmppb$OScensor, marker=tmppb$Biomarker1)
	o.data2 =data.frame(event=tmptrt$OSday, censor=tmptrt$OScensor, marker=tmptrt$Biomarker1)
	
        ## Draw the scattor plot for the three data objects:

	DuoScattorPlot(o.data1,o.data2,180,xlab=c("Marker"),ylab=c("Survival Rate"),
		main=c("Weighted Predictiveness Curve"),ylim=c(0,600),xlim=c(0,100),
		col1="red",col2="black",lwd=2,pch1=20,pch2=21,legendloc="bottomright",ncol=1)