tests/testthat/test-otherPackages.R

In bozenne/BuyseTest: Generalized Pairwise Comparisons

### test-otherPackages.R --- 
##----------------------------------------------------------------------
## Author: Brice Ozenne
## Created: maj 27 2018 (17:10) 
## Version: 
## Last-Updated: Jun  9 2023 (17:35) 
##           By: Brice Ozenne
##     Update #: 27
##----------------------------------------------------------------------
## 
### Commentary: 
## 
### Change Log:
##----------------------------------------------------------------------
## 
### Code:


if(FALSE){
    library(testthat)
    library(BuyseTest)
    library(data.table)
}

context("Comparison with other softwares")


## * other packages:
## ** winRatioAnalysis
## Author:
## David A. Schoenfeld

## Description:
## Fits a model to data separately for each treatment group and then calculates the win-
## Ratio as a function of follow-up time.

## Reference:
## Bebu I, Lachin JM. Large sample inference for a win ratio analysis of a composite outcome based
## on prioritized components. Biostatistics. 2015 Sep 8;17(1):178-87.

## library(winRatioAnalysis)

## winRatio(Surv(tdeath,cdeath),
         ## treatmentVariable='rx',
         ## treatmentCodes = c(1, 0),
         ## data=dat,
         ## secondSurvivalObject=Surv(tprog0,tprog1,type='interval'),
         ## pssmIntervals=1,
         ## method = "pssm",
         ## plotPoints =3,
         ## integrationIntervals=1)

## ** WWR
## Description:
## Calculate the (weighted) win loss statistics including the win ratio, win difference and win product
## and their variances, with which the p-values are also calculated.

## Author:
## Xiaodong Luo [aut, cre] et al.

## References:
## Pocock S.J., Ariti C.A., Collier T. J. and Wang D. 2012. The win ratio: a new approach to the anal-
## ysis 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.
## Bebu I. and Lachin J.M. 2016.   Large sample inference for a win ratio analysis of a composite
## outcome based on prioritized components. Biostatistics, 17, 178-187.
## Luo X., Qiu J., Bai S. and Tian H. 2017.  Weighted win loss approach for analyzing prioritized
## outcomes. Statistics in Medicine, <doi: 10.1002/sim.7284>

## library(WWR)

## set.seed(10)
## dt.sim <- rbind(data.table(time1 = runif(10,0,5),
                           ## status1 = 1,
                           ## time2 = 20,
                           ## status2 = 1,
                           ## treatment = 1),
                ## data.table(time1 = runif(10,0,5),
                           ## status1 = 1,
                           ## time2 = 0,
                           ## status2 = 1,
                           ## treatment = 0)
                ## )

## wtest <- WWR::winratio(y1 = dt.sim$time1,
                       ## y2 = dt.sim$time2,
                       ## d1 = dt.sim$status1,
                       ## d2 = dt.sim$status2,
                       ## z = dt.sim$treatment)

## summary(wtest)
## str(wtest)

## BT <- BuyseTest(treatment ~ tte(time1, status = status1) + tte(time2, status = status2),
                ## data = dt.sim,
                ## method.inference = "none")

## summary(BT, statistic = "winRatio", percentage = FALSE)
## 1



##  > results
##    endpoint threshold total favorable unfavorable neutral uninf    delta    Delta
## eventtime1     1e-12   400       287         113       0     0 2.539823 2.539823
## eventtime2     1e-12     0         0           0       0     0       NA 2.539823

## ** WLreg
## Description:
## Use various regression models for the analysis of win loss endpoints adjusting for non-binary and multivariate covariates.

## Author:
## Xiaodong Luo

## References:
## Pocock S.J., Ariti C.A., Collier T. J. and Wang D. 2012. The win ratio: a new approach to the anal-
## ysis 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.

## library(WLreg)

## set.seed(10)
## dt.sim <- rbind(data.table(time1 = runif(10,0,5),
##                            status1 = 1,
##                            time2 = 20,
##                            status2 = 1,
##                            treatment = 1),
##                 data.table(time1 = runif(10,0,5),
##                            status1 = 1,
##                            time2 = 0,
##                            status2 = 1,
##                            treatment = 0)
##                 )

## aa<-winreg(dt.sim$time1,
##            dt.sim$time2,
##            dt.sim$status1,
##            dt.sim$status2,
##            dt.sim$treatment)
## aa

## ** rankFD
## library(rankFD)
## calcSE <- function(x, y){
##     nx <- length(x)
##     ny <- length(y)
##     Nxy <- nx + ny ## total sample size
##     xy <- c(x, y) ## full sample
##     rxy <- rank(xy) ## rank in the full sample
##     rx <- rank(x) ## rank in the first sample
##     ry <- rank(y) ## rank in the second sample
##     plx <- 1/ny * (rxy[1:nx] - rx) ## difference rank sample 1 and rank whole sample
##     ply <- 1/nx * (rxy[(nx + 1):(Nxy)] - ry) ## difference rank sample 2 and rank whole sample
##     vx <- var(plx)
##     vy <- var(ply)
##     vxy <- Nxy * (vx/nx + vy/ny) ## variance
##     return(c(estimate = mean(plx), se = sqrt(vxy/Nxy), var = vxy/Nxy, vx = vx, vy = vy))
## }


## data(Muco)  
## Muco2 <- subset(Muco, Disease != "OAD")
## Muco2$Disease <- droplevels(Muco2$Disease)
## any(duplicated(Muco2$HalfTime))

## rank.two.samples(HalfTime ~ Disease, data = Muco2, wilcoxon = "asymptotic", permu = FALSE)$Analysis

## GS <- calcSE(x = Muco2[Muco2$Disease=="Normal","HalfTime"],
##              y = Muco2[Muco2$Disease=="Asbestosis","HalfTime"])
## GS
## sum(c(0.0120000 ,0.2520000))/5 

## GS <- BMstat2(x = Muco2[Muco2$Disease=="Normal","HalfTime"],
##               y = Muco2[Muco2$Disease=="Asbestosis","HalfTime"],
##               nx = 1,
##               ny = 1,
##               method = "t.app")

## sum(h1plus.trt^2)/(nt-1)

## test <- BuyseTest(Disease ~ cont(HalfTime), data = Muco2, trace = FALSE)
## confint(test, statistic = "favorable")

## resM <- NTB.Ustat.fun(Yt = Muco2[Muco2$Disease=="Normal","HalfTime"],
##                       Yc = Muco2[Muco2$Disease=="Asbestosis","HalfTime"],
##                       tau = 0.0000000000000001)

## sqrt(resM$Var.fav.order1.rank)

##----------------------------------------------------------------------
### test-otherPackages.R ends here