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

Description Usage Arguments Author(s) References See Also Examples

View source: R/DuoScattorPlot.R

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

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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

Author(s)

Hui Yang huiy@amgen.com, Rui Tang rui_tang@vrtx.com and Jing Huang jinghuang0@gmail.com

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

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	## 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)

WPC documentation built on May 2, 2019, 6:52 a.m.