# EmpiricalSurvDiff: Estimate the LR value and its associated p-values In FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ``` 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ``` ## 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 Jan. 13, 2021, 3:39 p.m.