WR.CRT-manual: Win Ratio analysis for composite endpoints of semi-competing...
In dee1008/cWR: Win Ratio Analysis
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
This function uses Win Ratio (WR) as a summary statistic to compare the composite endpoints of semi-competing risk time-to-event data between two groups.
Analysis can be done for the following scenarios:
(1) Independent subjects without confounders (randomized controlled trial)
(2) Cluster-randomized subjects without confounders (cluster randomized trial). The clustered structure is that all subjects within a cluster belong to the same comparison group. For instance, patients are nested within hospitals. Treatments are randomized to hospitals and all patients within a hospitcal receive the same treatment.
Usage
1
WR.CRT(treatment, cluster, y1, y2, delta1, delta2, null.WR=1, alpha.sig=0.05)
Arguments
treatment
Integer vector with code 0 as control group and 1 as treatment group for each subject
cluster
Integer vector with unique cluster ID for each cluster. When subjects are independent, the cluster ID is unique for each subject.
y1
Numeric vector with min(T_H, T_D, T_C) for each subject, where T_H, T_D and T_C are time to non-fatal event, time to fatal event and censoring time, respectively
y2
Numeric vector with min(T_D, T_C) for each subject
delta1
Integer vector with code 1 indicating that T_H is observed, 0 otherwise
delta2
Integer vector with code 1 indicating that T_D is observed, 0 otherwise
null.WR
Null hypothesis of the WR statistic. The default is H0: WR=1 or log(WR)=0.
alpha.sig
Significance level, with default value 0.05
Details
The function "WR.CRT" performs significance testing of comparing two composite time-to-event outcomes between groups. The Win Ratio summary statistic is built on the "unmatched" approach described by Pocock et al. (2011). We assume that the composite endpoints can be formulated as semi-competing risk data. Each individual in the study is measured on time to non-fatal (non-terminal) event (e.g. hospitalization) and time to fatal (terminal) event (e.g. death). Specifically, the fatal event is considered clinically more important compared to the non-fatal event. Censoring is allowed, but time to censor needs to be observed.
This function can handle independent data, as well as clustered data. The inference of clustered data is based on the generalized bivariate clustered U-statistics proposed by Zhang and Jeong (2019). This clustered U-statistic accounts for the potential correlations among subjects within a cluster. When the cluster size is 1, it's the independent setting and the inference is the same as the method proposed by Bebu and Lachin (2015).
Note: The option "treatment", "cluster", "y1", "y2", "delta1", "delta2" are required and no defaults are provided. These options have to be numeric vectors with the same length. No missing values are allowed.
Value
name
Test name
U1
First estimated clustered U-statistic
U2
Second estimated clustered U-statistic
logWR
Estimated WR on log scale
se
Estimated standard error of the WR on log scale
z
Test statistic
ci
100(1-alpha.sig)% confidence interval
p-value
P-value of the significance testing
var_cov
Variance and covariance matrix of the first and second clustered U-statistics
Author(s)
Di Zhang <diz11@pitt.edu>;
Jong H. Jeong <jjeong@pitt.edu>
Maintainer: Di Zhang <diz11@pitt.edu>
References
Pocock, S. J., Ariti, C. A., Collier, T. J., andWang, D. (2011). 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.
Bebu, I. and Lachin, J. M. (2015). Large sample inference for a win ratio analysis of a
composite outcome based on prioritized components. Biostatistics 17, 178-187.
Zhang, D. and Jeong, H. J. Inference on the Win Ratio for Clustered Semi-Competing Risk Data. (Submitted first revision to the Lifetime Data Analysis, February 2019)
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## Not run:
# load library
library(gumbel)
library(devtools)
# download and install package through Github
install_github("dee1008/cWR")
library(cWR)
set.seed(123)
#---------1. Data generation for independent semi-competing risk data-----
# joint survival: bivariate exponential with Gumbel-Hougaard copula
# define functions
gumbel_independent<-function(n,n.clust,dim,alpha,lambdaH,lambdaD,etaH,etaD)
{
exprand <- matrix(rexp(dim * n), c(n, dim))
unifpirand <- runif(n, 0, pi)
exprand2 <- rexp(n)
beta <- 1/alpha
stablerand <- sin((1 - beta) * unifpirand)^((1 - beta)/beta) *
(sin(beta * unifpirand))/(sin(unifpirand))^(1/beta)
stablerand <- stablerand/(exprand2^(alpha - 1))
unifrand <- invphigumbel(exprand/stablerand, alpha) # generating bivariate uniform random variables for marginal survival funtions--(*)
clust.siz<- n/n.clust
frail <- rep(rep(1, n.clust),each=clust.siz)
matrix(c(-log(unifrand[,1])/(frail*lambdaH*exp(-etaH)),-log(unifrand[,2])/(frail*lambdaD*exp(-etaD))),c(n,dim)) # inverting specific forms of survival functions in (*) to create
# true bivariate event times adjusted for event types and trt groups
}
gen_independent<-function(n.sub, n.clust, dim, alpha, lambdaH, lambdaD, lambdaC, etaH, etaD, etaC){
group0<-gumbel_independent(n.sub,n.clust,dim,alpha,lambdaH,lambdaD,0,0)
group1<-gumbel_independent(n.sub,n.clust,dim,alpha,lambdaH,lambdaD,etaH,etaD)
true.t<-rbind(group0,group1)
temp.data<-cbind(true.t,c(rexp(dim(true.t)[1]/2,lambdaC),rexp(dim(true.t)[1]/2,lambdaC*exp(-etaC))))
t.obs<-apply(temp.data,1,min)
delH<-rep(0,dim(true.t)[1])
delD<-rep(0,dim(true.t)[1])
delH[temp.data[,1]==t.obs]<-1
delD[temp.data[,2]==t.obs]<-1
my.data<-cbind(temp.data,t.obs,delH,delD,rep(0:1,each=dim(true.t)[1]/2))
y1<-rep(0,dim(true.t)[1])
y2<-rep(0,dim(true.t)[1])
my.data.f<-data.frame(cbind(my.data,y1,y2))
names(my.data.f)<-c("t1","t2","c","t.obs","delH","delD","group","y1","y2")
indi.1<-(my.data.f$c < my.data.f$t1) & (my.data.f$c < my.data.f$t2)
my.data.f$y1[indi.1]<-my.data.f$c[indi.1]
my.data.f$y2[indi.1]<-my.data.f$c[indi.1]
indi.2<-(my.data.f$t2 < my.data.f$t1) & (my.data.f$t1 < my.data.f$c)
indi.21<-(my.data.f$t2 < my.data.f$c) & (my.data.f$c < my.data.f$t1)
my.data.f$y1[indi.2 | indi.21]<-my.data.f$t2[indi.2| indi.21]
my.data.f$y2[indi.2| indi.21]<-my.data.f$t2[indi.2| indi.21]
indi.3<-(my.data.f$t1 < my.data.f$c) & (my.data.f$c < my.data.f$t2)
my.data.f$y1[indi.3]<-my.data.f$t1[indi.3]
my.data.f$y2[indi.3]<-my.data.f$c[indi.3]
indi.4<-(my.data.f$t1 < my.data.f$t2) & (my.data.f$t2 < my.data.f$c)
my.data.f$y1[indi.4]<-my.data.f$t1[indi.4]
my.data.f$y2[indi.4]<-my.data.f$t2[indi.4]
my.data.f$delD[indi.4]<-1
# add cluster information in the data set
my.data.f$cluster<-rep(1:(2*n.clust),each=n.sub/n.clust)
names(my.data.f)<-c("time_Non_Fatal","time_Fatal","time_censor","t.obs","delH","delD","treatment","y1","y2","cluster")
return(my.data.f)
}
# generate independent data
data1<-gen_independent(n.sub=100, n.clust=100, dim=2, alpha=2, lambdaH=0.1, lambdaD=0.08, lambdaC=0.09, etaH=0.2, etaD=0.5, etaC=0.1)
# independent win ratio
ind.wr<-with(data1, WR.CRT(treatment=treatment, cluster=cluster, y1=y1, y2=y2, delta1=delH, delta2=delD, null.WR=1,alpha.sig=0.05))
# logWR
ind.wr$logWR
# se of logWR
ind.wr$se
# 95% CI of logWR
ind.wr$ci
# p-value
ind.wr$p
#------2. Data generation for clustered semi-competing risk data--------
# joint survival: clustered bivariate exponential with Gumbel-Hougaard copula
# define functions
gumbel_cluster<-function(n,n.clust,dim,alpha,lambdaH,lambdaD,etaH,etaD,shape,rate)
{
exprand <- matrix(rexp(dim * n), c(n, dim))
unifpirand <- runif(n, 0, pi)
exprand2 <- rexp(n)
beta <- 1/alpha
stablerand <- sin((1 - beta) * unifpirand)^((1 - beta)/beta) *
(sin(beta * unifpirand))/(sin(unifpirand))^(1/beta)
stablerand <- stablerand/(exprand2^(alpha - 1))
unifrand <- invphigumbel(exprand/stablerand, alpha) # generating bivariate uniform random variables for marginal survival funtions--(*)
clust.siz<- n/n.clust
frail <- rep(rgamma(n.clust,shape=shape,rate=rate),each=clust.siz)
matrix(c(-log(unifrand[,1])/(frail*lambdaH*exp(-etaH)),-log(unifrand[,2])/(frail*lambdaD*exp(-etaD))),c(n,dim)) # inverting specific forms of survival functions in (*) to create
# true bivariate event times adjusted for event types and trt groups
}
gen_cluster<-function(n.sub, n.clust, dim, alpha, lambdaH, lambdaD, lambdaC, etaH, etaD, etaC, shape, rate){
group0<-gumbel_cluster(n.sub,n.clust,dim,alpha,lambdaH,lambdaD,0,0,shape,rate)
group1<-gumbel_cluster(n.sub,n.clust,dim,alpha,lambdaH,lambdaD,etaH,etaD,shape,rate)
true.t<-rbind(group0,group1)
temp.data<-cbind(true.t,c(rexp(dim(true.t)[1]/2,lambdaC),rexp(dim(true.t)[1]/2,lambdaC*exp(-etaC))))
t.obs<-apply(temp.data,1,min)
delH<-rep(0,dim(true.t)[1])
delD<-rep(0,dim(true.t)[1])
delH[temp.data[,1]==t.obs]<-1
delD[temp.data[,2]==t.obs]<-1
my.data<-cbind(temp.data,t.obs,delH,delD,rep(0:1,each=dim(true.t)[1]/2))
y1<-rep(0,dim(true.t)[1])
y2<-rep(0,dim(true.t)[1])
my.data.f<-data.frame(cbind(my.data,y1,y2))
names(my.data.f)<-c("t1","t2","c","t.obs","delH","delD","group","y1","y2")
indi.1<-(my.data.f$c < my.data.f$t1) & (my.data.f$c < my.data.f$t2)
my.data.f$y1[indi.1]<-my.data.f$c[indi.1]
my.data.f$y2[indi.1]<-my.data.f$c[indi.1]
indi.2<-(my.data.f$t2 < my.data.f$t1) & (my.data.f$t1 < my.data.f$c)
indi.21<-(my.data.f$t2 < my.data.f$c) & (my.data.f$c < my.data.f$t1)
my.data.f$y1[indi.2 | indi.21]<-my.data.f$t2[indi.2| indi.21]
my.data.f$y2[indi.2| indi.21]<-my.data.f$t2[indi.2| indi.21]
indi.3<-(my.data.f$t1 < my.data.f$c) & (my.data.f$c < my.data.f$t2)
my.data.f$y1[indi.3]<-my.data.f$t1[indi.3]
my.data.f$y2[indi.3]<-my.data.f$c[indi.3]
indi.4<-(my.data.f$t1 < my.data.f$t2) & (my.data.f$t2 < my.data.f$c)
my.data.f$y1[indi.4]<-my.data.f$t1[indi.4]
my.data.f$y2[indi.4]<-my.data.f$t2[indi.4]
my.data.f$delD[indi.4]<-1
# add cluster information in the data set
my.data.f$cluster<-rep(1:(2*n.clust),each=n.sub/n.clust)
names(my.data.f)<-c("time_Non_Fatal","time_Fatal","time_censor","t.obs","delH","delD","treatment","y1","y2","cluster")
return(my.data.f)
}
# generate clustered data
data2<-gen_cluster(n.sub=500, n.clust=25, dim=2, alpha=2, lambdaH=0.1, lambdaD=0.08, lambdaC=0.09, etaH=0.2, etaD=0.5, etaC=0.1, shape=15, rate=15)
# clustered win ratio
clus.wr<-with(data2, WR.CRT(treatment=treatment, cluster=cluster, y1=y1, y2=y2, delta1=delH, delta2=delD, null.WR=1,alpha.sig=0.05))
# logWR
clus.wr$logWR
# se of logWR
clus.wr$se
# 95% CI of logWR
clus.wr$ci
# p-value
clus.wr$p
## End(Not run)
dee1008/cWR documentation built on May 8, 2019, 7:27 a.m.
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Description Usage Arguments Details Value Author(s) References Examples
Description
This function uses Win Ratio (WR) as a summary statistic to compare the composite endpoints of semi-competing risk time-to-event data between two groups.
Analysis can be done for the following scenarios: (1) Independent subjects without confounders (randomized controlled trial) (2) Cluster-randomized subjects without confounders (cluster randomized trial). The clustered structure is that all subjects within a cluster belong to the same comparison group. For instance, patients are nested within hospitals. Treatments are randomized to hospitals and all patients within a hospitcal receive the same treatment.
Usage
1 | WR.CRT(treatment, cluster, y1, y2, delta1, delta2, null.WR=1, alpha.sig=0.05)
|
Arguments
treatment |
Integer vector with code 0 as control group and 1 as treatment group for each subject |
cluster |
Integer vector with unique cluster ID for each cluster. When subjects are independent, the cluster ID is unique for each subject. |
y1 |
Numeric vector with min(T_H, T_D, T_C) for each subject, where T_H, T_D and T_C are time to non-fatal event, time to fatal event and censoring time, respectively |
y2 |
Numeric vector with min(T_D, T_C) for each subject |
delta1 |
Integer vector with code 1 indicating that T_H is observed, 0 otherwise |
delta2 |
Integer vector with code 1 indicating that T_D is observed, 0 otherwise |
null.WR |
Null hypothesis of the WR statistic. The default is H0: WR=1 or log(WR)=0. |
alpha.sig |
Significance level, with default value 0.05 |
Details
The function "WR.CRT" performs significance testing of comparing two composite time-to-event outcomes between groups. The Win Ratio summary statistic is built on the "unmatched" approach described by Pocock et al. (2011). We assume that the composite endpoints can be formulated as semi-competing risk data. Each individual in the study is measured on time to non-fatal (non-terminal) event (e.g. hospitalization) and time to fatal (terminal) event (e.g. death). Specifically, the fatal event is considered clinically more important compared to the non-fatal event. Censoring is allowed, but time to censor needs to be observed.
This function can handle independent data, as well as clustered data. The inference of clustered data is based on the generalized bivariate clustered U-statistics proposed by Zhang and Jeong (2019). This clustered U-statistic accounts for the potential correlations among subjects within a cluster. When the cluster size is 1, it's the independent setting and the inference is the same as the method proposed by Bebu and Lachin (2015).
Note: The option "treatment", "cluster", "y1", "y2", "delta1", "delta2" are required and no defaults are provided. These options have to be numeric vectors with the same length. No missing values are allowed.
Value
name |
Test name |
U1 |
First estimated clustered U-statistic |
U2 |
Second estimated clustered U-statistic |
logWR |
Estimated WR on log scale |
se |
Estimated standard error of the WR on log scale |
z |
Test statistic |
ci |
100(1-alpha.sig)% confidence interval |
p-value |
P-value of the significance testing |
var_cov |
Variance and covariance matrix of the first and second clustered U-statistics |
Author(s)
Di Zhang <diz11@pitt.edu>; Jong H. Jeong <jjeong@pitt.edu>
Maintainer: Di Zhang <diz11@pitt.edu>
References
Pocock, S. J., Ariti, C. A., Collier, T. J., andWang, D. (2011). 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.
Bebu, I. and Lachin, J. M. (2015). Large sample inference for a win ratio analysis of a composite outcome based on prioritized components. Biostatistics 17, 178-187.
Zhang, D. and Jeong, H. J. Inference on the Win Ratio for Clustered Semi-Competing Risk Data. (Submitted first revision to the Lifetime Data Analysis, February 2019)
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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | ## Not run:
# load library
library(gumbel)
library(devtools)
# download and install package through Github
install_github("dee1008/cWR")
library(cWR)
set.seed(123)
#---------1. Data generation for independent semi-competing risk data-----
# joint survival: bivariate exponential with Gumbel-Hougaard copula
# define functions
gumbel_independent<-function(n,n.clust,dim,alpha,lambdaH,lambdaD,etaH,etaD)
{
exprand <- matrix(rexp(dim * n), c(n, dim))
unifpirand <- runif(n, 0, pi)
exprand2 <- rexp(n)
beta <- 1/alpha
stablerand <- sin((1 - beta) * unifpirand)^((1 - beta)/beta) *
(sin(beta * unifpirand))/(sin(unifpirand))^(1/beta)
stablerand <- stablerand/(exprand2^(alpha - 1))
unifrand <- invphigumbel(exprand/stablerand, alpha) # generating bivariate uniform random variables for marginal survival funtions--(*)
clust.siz<- n/n.clust
frail <- rep(rep(1, n.clust),each=clust.siz)
matrix(c(-log(unifrand[,1])/(frail*lambdaH*exp(-etaH)),-log(unifrand[,2])/(frail*lambdaD*exp(-etaD))),c(n,dim)) # inverting specific forms of survival functions in (*) to create
# true bivariate event times adjusted for event types and trt groups
}
gen_independent<-function(n.sub, n.clust, dim, alpha, lambdaH, lambdaD, lambdaC, etaH, etaD, etaC){
group0<-gumbel_independent(n.sub,n.clust,dim,alpha,lambdaH,lambdaD,0,0)
group1<-gumbel_independent(n.sub,n.clust,dim,alpha,lambdaH,lambdaD,etaH,etaD)
true.t<-rbind(group0,group1)
temp.data<-cbind(true.t,c(rexp(dim(true.t)[1]/2,lambdaC),rexp(dim(true.t)[1]/2,lambdaC*exp(-etaC))))
t.obs<-apply(temp.data,1,min)
delH<-rep(0,dim(true.t)[1])
delD<-rep(0,dim(true.t)[1])
delH[temp.data[,1]==t.obs]<-1
delD[temp.data[,2]==t.obs]<-1
my.data<-cbind(temp.data,t.obs,delH,delD,rep(0:1,each=dim(true.t)[1]/2))
y1<-rep(0,dim(true.t)[1])
y2<-rep(0,dim(true.t)[1])
my.data.f<-data.frame(cbind(my.data,y1,y2))
names(my.data.f)<-c("t1","t2","c","t.obs","delH","delD","group","y1","y2")
indi.1<-(my.data.f$c < my.data.f$t1) & (my.data.f$c < my.data.f$t2)
my.data.f$y1[indi.1]<-my.data.f$c[indi.1]
my.data.f$y2[indi.1]<-my.data.f$c[indi.1]
indi.2<-(my.data.f$t2 < my.data.f$t1) & (my.data.f$t1 < my.data.f$c)
indi.21<-(my.data.f$t2 < my.data.f$c) & (my.data.f$c < my.data.f$t1)
my.data.f$y1[indi.2 | indi.21]<-my.data.f$t2[indi.2| indi.21]
my.data.f$y2[indi.2| indi.21]<-my.data.f$t2[indi.2| indi.21]
indi.3<-(my.data.f$t1 < my.data.f$c) & (my.data.f$c < my.data.f$t2)
my.data.f$y1[indi.3]<-my.data.f$t1[indi.3]
my.data.f$y2[indi.3]<-my.data.f$c[indi.3]
indi.4<-(my.data.f$t1 < my.data.f$t2) & (my.data.f$t2 < my.data.f$c)
my.data.f$y1[indi.4]<-my.data.f$t1[indi.4]
my.data.f$y2[indi.4]<-my.data.f$t2[indi.4]
my.data.f$delD[indi.4]<-1
# add cluster information in the data set
my.data.f$cluster<-rep(1:(2*n.clust),each=n.sub/n.clust)
names(my.data.f)<-c("time_Non_Fatal","time_Fatal","time_censor","t.obs","delH","delD","treatment","y1","y2","cluster")
return(my.data.f)
}
# generate independent data
data1<-gen_independent(n.sub=100, n.clust=100, dim=2, alpha=2, lambdaH=0.1, lambdaD=0.08, lambdaC=0.09, etaH=0.2, etaD=0.5, etaC=0.1)
# independent win ratio
ind.wr<-with(data1, WR.CRT(treatment=treatment, cluster=cluster, y1=y1, y2=y2, delta1=delH, delta2=delD, null.WR=1,alpha.sig=0.05))
# logWR
ind.wr$logWR
# se of logWR
ind.wr$se
# 95% CI of logWR
ind.wr$ci
# p-value
ind.wr$p
#------2. Data generation for clustered semi-competing risk data--------
# joint survival: clustered bivariate exponential with Gumbel-Hougaard copula
# define functions
gumbel_cluster<-function(n,n.clust,dim,alpha,lambdaH,lambdaD,etaH,etaD,shape,rate)
{
exprand <- matrix(rexp(dim * n), c(n, dim))
unifpirand <- runif(n, 0, pi)
exprand2 <- rexp(n)
beta <- 1/alpha
stablerand <- sin((1 - beta) * unifpirand)^((1 - beta)/beta) *
(sin(beta * unifpirand))/(sin(unifpirand))^(1/beta)
stablerand <- stablerand/(exprand2^(alpha - 1))
unifrand <- invphigumbel(exprand/stablerand, alpha) # generating bivariate uniform random variables for marginal survival funtions--(*)
clust.siz<- n/n.clust
frail <- rep(rgamma(n.clust,shape=shape,rate=rate),each=clust.siz)
matrix(c(-log(unifrand[,1])/(frail*lambdaH*exp(-etaH)),-log(unifrand[,2])/(frail*lambdaD*exp(-etaD))),c(n,dim)) # inverting specific forms of survival functions in (*) to create
# true bivariate event times adjusted for event types and trt groups
}
gen_cluster<-function(n.sub, n.clust, dim, alpha, lambdaH, lambdaD, lambdaC, etaH, etaD, etaC, shape, rate){
group0<-gumbel_cluster(n.sub,n.clust,dim,alpha,lambdaH,lambdaD,0,0,shape,rate)
group1<-gumbel_cluster(n.sub,n.clust,dim,alpha,lambdaH,lambdaD,etaH,etaD,shape,rate)
true.t<-rbind(group0,group1)
temp.data<-cbind(true.t,c(rexp(dim(true.t)[1]/2,lambdaC),rexp(dim(true.t)[1]/2,lambdaC*exp(-etaC))))
t.obs<-apply(temp.data,1,min)
delH<-rep(0,dim(true.t)[1])
delD<-rep(0,dim(true.t)[1])
delH[temp.data[,1]==t.obs]<-1
delD[temp.data[,2]==t.obs]<-1
my.data<-cbind(temp.data,t.obs,delH,delD,rep(0:1,each=dim(true.t)[1]/2))
y1<-rep(0,dim(true.t)[1])
y2<-rep(0,dim(true.t)[1])
my.data.f<-data.frame(cbind(my.data,y1,y2))
names(my.data.f)<-c("t1","t2","c","t.obs","delH","delD","group","y1","y2")
indi.1<-(my.data.f$c < my.data.f$t1) & (my.data.f$c < my.data.f$t2)
my.data.f$y1[indi.1]<-my.data.f$c[indi.1]
my.data.f$y2[indi.1]<-my.data.f$c[indi.1]
indi.2<-(my.data.f$t2 < my.data.f$t1) & (my.data.f$t1 < my.data.f$c)
indi.21<-(my.data.f$t2 < my.data.f$c) & (my.data.f$c < my.data.f$t1)
my.data.f$y1[indi.2 | indi.21]<-my.data.f$t2[indi.2| indi.21]
my.data.f$y2[indi.2| indi.21]<-my.data.f$t2[indi.2| indi.21]
indi.3<-(my.data.f$t1 < my.data.f$c) & (my.data.f$c < my.data.f$t2)
my.data.f$y1[indi.3]<-my.data.f$t1[indi.3]
my.data.f$y2[indi.3]<-my.data.f$c[indi.3]
indi.4<-(my.data.f$t1 < my.data.f$t2) & (my.data.f$t2 < my.data.f$c)
my.data.f$y1[indi.4]<-my.data.f$t1[indi.4]
my.data.f$y2[indi.4]<-my.data.f$t2[indi.4]
my.data.f$delD[indi.4]<-1
# add cluster information in the data set
my.data.f$cluster<-rep(1:(2*n.clust),each=n.sub/n.clust)
names(my.data.f)<-c("time_Non_Fatal","time_Fatal","time_censor","t.obs","delH","delD","treatment","y1","y2","cluster")
return(my.data.f)
}
# generate clustered data
data2<-gen_cluster(n.sub=500, n.clust=25, dim=2, alpha=2, lambdaH=0.1, lambdaD=0.08, lambdaC=0.09, etaH=0.2, etaD=0.5, etaC=0.1, shape=15, rate=15)
# clustered win ratio
clus.wr<-with(data2, WR.CRT(treatment=treatment, cluster=cluster, y1=y1, y2=y2, delta1=delH, delta2=delD, null.WR=1,alpha.sig=0.05))
# logWR
clus.wr$logWR
# se of logWR
clus.wr$se
# 95% CI of logWR
clus.wr$ci
# p-value
clus.wr$p
## End(Not run)
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