winreg {WLreg} | R Documentation |
Use two Cox regression models (one for the terminal event and the other for the non-trminal event) to model the win product adjusting for covariates
winreg(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 else. |
d2 |
a numeric vector of event indicators with 1 denoting the terminal event is observed and 0 else. |
z |
a numeric matrix of covariates. |
This function uses two Cox regression models (one for the terminal event and the other for the non-trminal event) to model the win product adjusting for covariates.
beta1 |
Estimated regression parameter based on the non-terminal event times |
sigma1 |
Estimated variance of |
tb1 |
Wald test statistics based on |
pb1 |
Two-sided p-values of the Wald test statistics |
beta2 |
Estimated regression parameter based on the terminal event times |
sigma2 |
Estimated variance of |
tb2 |
Wald test statistics based on |
pb2 |
Two-sided p-values of the Wald test statistics |
beta |
|
sigma |
Estimated variance of |
tb |
Wald test statistics based on |
pb |
Two-sided p-values of the Wald test statistics |
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.
Luo X., Qiu J., Bai S. and Tian H. 2017. Weighted win loss approach for analyzing prioritized outcomes. Statistics in Medicine, to appear.
###Generate data n<-300 rho<-0.5 b2<-c(1.0,-1.0) b1<-c(0.5,-0.9) bc<-c(1.0,0.5) lambda10<-0.1;lambda20<-0.08;lambdac0<-0.09 lam1<-rep(0,n);lam2<-rep(0,n);lamc<-rep(0,n) z1<-rep(0,n) z1[1:(n/2)]<-1 z2<-rnorm(n) z<-cbind(z1,z2) lam1<-lam2<-lamc<-rep(0,n) for (i in 1:n){ lam1[i]<-lambda10*exp(-sum(z[i,]*b1)) lam2[i]<-lambda20*exp(-sum(z[i,]*b2)) lamc[i]<-lambdac0*exp(-sum(z[i,]*bc)) } 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) y<-cbind(y1,y2,d1,d2) z<-as.matrix(z) aa<-winreg(y1,y2,d1,d2,z) aa