# wlogr2: Log-rank statistics In WWR: Weighted Win Loss Statistics and their Variances

## Description

This will calculate the log-rank and Gehan statistics along with their variances

## Usage

 `1` ```wlogr2(y, d, z, wty = 1) ```

## Arguments

 `y` a vector of observed event times `d` a vector of event indicators with 1=event and 0=censored `z` a vector of group indicators with 1=treatment and 0=control `wty` a vector of weight indicators with 1=Gehan and 2=log-rank

## Value

 `wty` Type of statistics, 1=Gehan, 2=log-rank `stat` value of the stat `vstat` estimated variance `tstat` standardized test stat `pstat` 2-sided p-value of the standardized test stat

## Note

This provides Gehan test that is usually ignored

Xiaodong Luo

## References

Gehan E.A. 1965. A generalized Wilcoxon test for comparing arbitrarily single-censored samples. Biometrika, 53, 203-223.

Peto R. and Peto J. 1972. Asymptotically Efficient Rank Invariant Test Procedures. Journal of the Royal Statistical Society, Series A, 135, 185-207.

`winratio`,`wwratio`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```n<-300 b<-0.2 bc<-1.0 lambda0<-0.1;lambdac0<-0.09 lam<-rep(0,n);lamc<-rep(0,n) z<-rep(0,n) z[1:(n/2)]<-1 lam<-lambda0*exp(-b*z) lamc<-lambdac0*exp(-bc*z) tem<-matrix(0,ncol=2,nrow=n) tem[,1]<--log(1-runif(n))/lam tem[,2]<--log(1-runif(n))/lamc y<-apply(tem,1,min) d<-as.numeric(tem[,1]<=y) i<-1 ##i=1,2 wtest<-wlogr2(y,d,z,wty=i) wtest ```

### Example output

```\$wty
[1] 1

\$stat
[1] -10.42333

\$vstat
[1] 0.0493617

\$tstat
[1] -2.708638

\$pstat
[1] 0.006756006
```

WWR documentation built on May 2, 2019, 11:02 a.m.