FRESA.CAD (version 3.3.0)

EmpiricalSurvDiff: Estimate the LR value and its associated p-values

Description

Permutations or Bootstrapping computation of the standardized log-rank (SLR) or the Chi=SLR^2 p-values for differences in survival times

Usage

EmpiricalSurvDiff(times=times,
	                  status=status,
	                  groups=groups,
	                  samples=1000,
	                  type=c("SLR","Chi"),
	                  plots=FALSE,
	                  minAproxSamples=100,
	                  computeDist=FALSE,
	                  ...
	                  )

Arguments

times

A numeric vector with he observed times to event

status

A numeric vector indicating if the time to event is censored

groups

A numeric vector indicating the label of the two survival groups

samples

The number of bootstrap samples

type

The type of log-rank statistics. SLR or Chi

plots

If TRUE, the Kaplan-Meier plot will be plotted

minAproxSamples

The number of tail samples used for the normal-distribution approximation

computeDist

If TRUE, it will compute the bootstrapped distribution of the SLR

...

Additional parameters for the plot

Value

pvalue

the minimum one-tailed p-value : min[p(SRL < 0),p(SRL > 0)] for type="SLR" or the two tailed p-value: 1-p(|SRL| > 0) for type="Chi"

LR

A list of LR statistics: LR=Expected, VR=Variance, SLR=Standardized LR.

p.equal

The two tailed p-value: 1-p(|SRL| > 0)

p.sup

The one tailed p-value: p(SRL < 0), return NA for type="Chi"

p.inf

The one tailed p-value: p(SRL > 0), return NA for type="Chi"

nullDist

permutation derived probability density function of the null distribution

LRDist

bootstrapped derived probability density function of the SLR (computeDist=TRUE)

Details

It will compute the null distribution of the SRL or the square SLR (Chi) via permutations, and it will return the p-value of differences between survival times between two groups. It may also be used to compute the empirical distribution of the difference in SLR using bootstrapping. (computeDist=TRUE) The p-values will be estimated based on the sampled distribution, or normal-approximated along the tails.

Examples

# NOT RUN {
	
# }
# NOT RUN {
      library(rpart)
      data(stagec)

      # The Log-Rank Analysis using survdiff

      lrsurvdiff <- survdiff(Surv(pgtime,pgstat)~grade>2,data=stagec)
      print(lrsurvdiff)

      # The Log-Rank Analysis: permutations of the null Chi distribution
      lrp <- EmpiricalSurvDiff(stagec$pgtime,stagec$pgstat,stagec$grade>2,
                         type="Chi",plots=TRUE,samples=10000,
                         main="Chi Null Distribution")
      print(list(unlist(c(lrp$LR,lrp$pvalue))))

      # The Log-Rank Analysis: permutations of the null SLR distribution
      lrp <- EmpiricalSurvDiff(stagec$pgtime,stagec$pgstat,stagec$grade>2,
                         type="SLR",plots=TRUE,samples=10000,
                         main="SLR Null Distribution")
      print(list(unlist(c(lrp$LR,lrp$pvalue))))

      # The Log-Rank Analysis: Bootstraping the SLR distribution
      lrp <- EmpiricalSurvDiff(stagec$pgtime,stagec$pgstat,stagec$grade>2,
                         computeDist=TRUE,plots=TRUE,samples=100000,
                         main="SLR Null and SLR bootrapped")
      print(list(unlist(c(lrp$LR,lrp$pvalue))))
	
	
# }