FRESA.CAD (version 3.4.8)

RRPlot: Plot and Analysis of Indices of Risk

Description

Plots of calibration and performance of risk probabilites

Usage

RRPlot(riskData=NULL,
	timetoEvent=NULL,
	riskTimeInterval=NULL,
	ExpectedPrevalence=NULL,
	atRate=c(0.90,0.80),
	atThr=NULL,
    plotRR=TRUE,
	title="",
	ysurvlim=c(0,1.0)
	)

Value

CumulativeOvs

Matrix with the Cumulative and Observed Events

OEData

Matrix with the Estimated and Observed Events

DCA

Decision Curve Analysis data matrix

RRData

The risk ratios data matrix for the ploted observations

timetoEventData

The dataframe with hazards, class and expeted time to event

keyPoints

The threshold values and metrics at: Specified, Max BACC, Max RR, and 100

OERatio

The Observed/Expected poisson test

OE95ci

The mean OE Ratio over the top 90

OARatio

The Observed/Accumlated poisson test

OAcum95ci

The mean O/A Ratio over the top 90

fit

The loess fit of the Risk Ratios

ROCAnalysis

The Reciver Operating Curve and Binary performance analysis

prevalence

The prevalence of events

thr_atP

The p-value that contains atProb of the negative subjects

c.index

The c-index with 90

surfit

The survival fit object

surdif

The logrank test analysis

LogRankE

The bootstreped p-value of the logrank test

Arguments

riskData

The data frame with two columns: First: Event label (event=1, censored=0). Second: Probability of any future event within the riskTimeInterval

timetoEvent

The time to event vector

riskTimeInterval

The time interval of the probability estimations

ExpectedPrevalence

For Case-Control Studies: The expected prevalence of events.

atRate

The desired TNR (specificity) or FNR (1.0-sensitivity) of the computed risk at threshold

atThr

The risk threshold

plotRR

If set to FALSE it will not generate the plots

title

The title postfix to be appended on each one of the generated plot titles

ysurvlim

The y limits of the survival plot

Author

Jose G. Tamez-Pena

Details

The RRPlot function will analyze the provided probabilities of risk and its associated events to generate calibration plots and plots of Relative Risk (RR) vs all the sensitivity values. Furthermore, it will compute and analyze the RR of the computed threshold that contains the prescribed rate of true negative cases (TNR) or if the atRate value is lower than 0.5 it will assume that it is the FNR (1-Specificity). If the user provides the time to event data, the function will also plot the Kaplan-Meier curve and return the logrank probability of differences between risk categories. For the calibration plot it will use the user provided riskTimeInterval to get the expected number of events. If the user does not provide the riskTimeInterval the function will use the maximum time of observations with events.

See Also

EmpiricalSurvDiff

Examples

	if (FALSE) {

        ### RR Plot Example ####
        # Start the graphics device driver to save all plots in a pdf format
        pdf(file = "RRPlot.pdf",width = 8, height = 6)

		library(survival)
		library(FRESA.CAD)

		op <- par(no.readonly = TRUE)

		### Libraries

		data(cancer, package="survival")
		lungD <- lung
		lungD$inst <- NULL
		lungD$status <- lungD$status - 1
		lungD <- lungD[complete.cases(lungD),]


		## Exploring Raw Features with RRPlot

		convar <- colnames(lungD)[lapply(apply(lungD,2,unique),length) > 10]
		convar <- convar[convar != "time"]
		topvar <- univariate_BinEnsemble(lungD[,c("status",convar)],"status")
		print(names(topvar))
		topv <- min(5,length(topvar))
		topFive <- names(topvar)[1:topv]
		RRanalysis <- list();
		idx <- 1
		for (topf in topFive)
		{
		  RRanalysis[[idx]] <- RRPlot(cbind(lungD$status,lungD[,topf]),
									  atRate=c(0.90),
									  timetoEvent=lungD$time,
									  title=topf,
									  # plotRR=FALSE
		  )
		  idx <- idx + 1
		}
		names(RRanalysis) <- topFive

		## Reporting the Metrics

		ROCAUC <- NULL
		CstatCI <- NULL
		LogRangp <- NULL
		Sensitivity <- NULL
		Specificity <- NULL

		for (topf in topFive)
		{
		  CstatCI <- rbind(CstatCI,RRanalysis[[topf]]$c.index$cstatCI)
		  LogRangp <- rbind(LogRangp,RRanalysis[[topf]]$surdif$pvalue)
		  Sensitivity <- rbind(Sensitivity,RRanalysis[[topf]]$ROCAnalysis$sensitivity)
		  Specificity <- rbind(Specificity,RRanalysis[[topf]]$ROCAnalysis$specificity)
		  ROCAUC <- rbind(ROCAUC,RRanalysis[[topf]]$ROCAnalysis$aucs)
		}
		rownames(CstatCI) <- topFive
		rownames(LogRangp) <- topFive
		rownames(Sensitivity) <- topFive
		rownames(Specificity) <- topFive
		rownames(ROCAUC) <- topFive

		print(ROCAUC)
		print(CstatCI)
		print(LogRangp)
		print(Sensitivity)
		print(Specificity)

		meanMatrix <- cbind(ROCAUC[,1],CstatCI[,1],Sensitivity[,1],Specificity[,1])
		colnames(meanMatrix) <- c("ROCAUC","C-Stat","Sen","Spe")
		print(meanMatrix)

		## COX Modeling
		ml <- BSWiMS.model(Surv(time,status)~1,data=lungD,NumberofRepeats = 10)
		sm <- summary(ml)
		print(sm$coefficients)

		### Cox Model Performance


		timeinterval <- 2*mean(subset(lungD,status==1)$time)

		h0 <- sum(lungD$status & lungD$time <= timeinterval)
		h0 <- h0/sum((lungD$time > timeinterval) | (lungD$status==1))
		print(t(c(h0=h0,timeinterval=timeinterval)),caption="Initial Parameters")

		index <- predict(ml,lungD)

		rdata <- cbind(lungD$status,ppoisGzero(index,h0))

		rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
								  timetoEvent=lungD$time,
								  title="Raw Train: lung Cancer",
								  ysurvlim=c(0.00,1.0),
								  riskTimeInterval=timeinterval)


		### Reporting Performance 


		print(rrAnalysisTrain$keyPoints,caption="Key Values")
		print(rrAnalysisTrain$OERatio,caption="O/E Test")
		print(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
		print(rrAnalysisTrain$OARatio,caption="O/Acum Test")
		print(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
		print(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
		print(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
		print((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
		print((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
		print(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
		print(rrAnalysisTrain$surdif,caption="Logrank test")


  
        dev.off()
	}