Utility function to generate tail-oriented windows inputs given the approximate number of subpopulations desired.
gen.tailwin(covariate, nsub, dir="LE")
Arguments
covariate: covariate values
nsub: number of tail-oriented subpopulations to be generated
dir: "LE" (default) or "GE" - subpopulations with covariate values less than or equal/greater than or equal to the generated values
Returns
It returns a list with fields: v−vectorofcovariatevaluestobeusedintheconstructorstepp.win.np - vector of subpopulation size associate with each tail-oriented window defined by $v.
Details
Use this together with the constructor, stepp.win, to generate tail-oriented windows.
Author(s)
Wai-ki Yip
See Also
stwin, stepp.win
Examples
data(bigKM)rxgroup <- bigKM$trt
time <- bigKM$time
evt <- bigKM$event
cov <- bigKM$ki67
# analyze using Kaplan-Meier method with tail-oriented window#nsubpop_tmp <-10win_tmp <- gen.tailwin(cov, nsub = nsubpop_tmp, dir ="LE")nsubpop <- length(win_tmp$v)# create a tail-oriented windowswin <- new("stwin", type ="tail-oriented", r1 = win_tmp$v, r2 = rep(min(cov), nsubpop))subp <- new("stsubpop")# create subpopulation objectsubp <- generate(subp, win = swin, covariate = cov)# generate the subpopulationssummary(subp)# summary of the subpopulations# create a stepp model using Kaplan Meier Method to analyze the data#smodel <- new("stmodelKM", coltrt=rxgroup, trts=c(1,2), survTime=time, censor=evt, timePoint=4)statKM <- new("steppes")# create a test object based on subpopulation and windowstatKM <- estimate(statKM, subp, smodel)# estimate the subpopulation results# Warning: IT IS RECOMMEND TO USE AT LEAST 2500 PERMUTATIONS TO PROVIDE STABLE RESULTS.statKM <- test(statKM, nperm =10)# permutation test with 10 iterationsprint(statKM)# print the estimates and test statisticsplot(statKM, ncex=0.65, legendy=30, pline=-15.5, color=c("blue","gold"), pointwise=FALSE, xlabel="Median Ki-67 LI in Subpopulation (% immunoreactivity)", ylabel="4-year Disease Free Survival", tlegend=c("Letrozole","Tamoxifen"), nlas=3)