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

In FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis

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

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.

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)

Author(s)

Jose G. Tamez-Pena

Examples

	## 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))))
	
	
## End(Not run)

FRESA.CAD documentation built on Nov. 25, 2023, 1:07 a.m.