winratio: Win Loss Statistics
In WWR: Weighted Win Loss Statistics and their Variances

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/winratio.R

Calculate the win loss statistics of Pocock et al. (2012) and the corresponding variances, which are based on a U-statistic method of Luo et al. (2015)

1	winratio(y1,y2,d1,d2,z)

`y1`	a numeric vector of event times denoting the minimum of event times T_1, T_2 and censoring time C, where the endpoint T_2, corresponding to the terminal event, is considered of higher clinical importance than the endpoint T_1, corresponding to the non-terminal event. Note that the terminal event may censor the non-terminal event, resulting in informative censoring.
`y2`	a numeric vector of event times denoting the minimum of event time T_2 and censoring time C. Clearly, y2 is not smaller than y1.
`d1`	a numeric vector of event indicators with 1 denoting the non-terminal event is observed and 0 denoting otherwise.
`d2`	a numeric vector of event indicators with 1 denoting the terminal event is observed and 0 denoting otherwise.Note that Luo et al. (2015) use a single indicator d so that d=1 if and only if `d1`=1 and `d2`=1; d=2 if and only if `d1`=0 and `d2`=1; d=3 if and only if `d1`=0 and `d2`=0; and d=4 if and only if `d1`=1 and `d2`=0.
`z`	a numeric vector of group indicators with 1 denoting the treatment group and 0 the control group.

win loss statistics

`n1`	Number of subjects in group 1
`n0`	Number of subjects in group 0
`n`	Total number of subjects in both groups
`totalw`	Total number of wins in group 1
`totall`	Total number of losses in group 1
`tw`	A vector of total numbers of wins in group 1 for each of the two outcomes. Note that `totalw`=sum(`tw`), and the first element is for the terminal event and the second element is for the non-terminal event.
`tl`	A vector of total numbers of losses in group 1 for each of the two outcomes. Note that `totall`=sum(`tl`), and the first element is for the terminal event and the second element is for the non-terminal event.
`xp`	The ratios between `tw` and `tl`
`cwindex`	The win contribution index defined as the ratio between `tw` and `totalw`+`totall`
`clindex`	The loss contribution index defined as the ratio between `tl` and `totalw`+`totall`
`wr`	win ratio
`vr`	estimated variance of win ratio
`tr`	standardized log(wr)
`pr`	2-sided p-value of tr
`wd`	win difference
`vd`	estimated variance of win difference
`td`	standardized wd
`pd`	2-sided p-value of td
`wp`	win product
`vp`	estimated variance of win product
`tp`	standardized log(wp)
`pp`	2-sided p-value of tp

Xiaodong Luo

Pocock S.J., Ariti C.A., Collier T. J. and Wang D. 2012. The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. European Heart Journal, 33, 176-182.

Luo X., Tian H., Mohanty S. and Tsai W.-Y. 2015. An alternative approach to confidence interval estimation for the win ratio statistic. Biometrics, 71, 139-145.

wlogr2,wwratio

n<-300
rho<-0.5
b2<-0.2
b1<-0.5
bc<-1.0
lambda10<-0.1;lambda20<-0.08;lambdac0<-0.09
lam1<-rep(0,n);lam2<-rep(0,n);lamc<-rep(0,n)
z<-rep(0,n)
z[1:(n/2)]<-1

lam1<-lambda10*exp(-b1*z)
lam2<-lambda20*exp(-b2*z)
lamc<-lambdac0*exp(-bc*z)
tem<-matrix(0,ncol=3,nrow=n)

y2y<-matrix(0,nrow=n,ncol=3)
y2y[,1]<-rnorm(n);y2y[,3]<-rnorm(n)
y2y[,2]<-rho*y2y[,1]+sqrt(1-rho^2)*y2y[,3]
tem[,1]<--log(1-pnorm(y2y[,1]))/lam1
tem[,2]<--log(1-pnorm(y2y[,2]))/lam2
tem[,3]<--log(1-runif(n))/lamc

y1<-apply(tem,1,min)
y2<-apply(tem[,2:3],1,min)
d1<-as.numeric(tem[,1]<=y1)
d2<-as.numeric(tem[,2]<=y2)
wtest<-winratio(y1,y2,d1,d2,z)
summary(wtest)