# Class `"stmodelKM"` This is the S4 class for the stepp model of survival data using Kaplan-Meier method. 1.1 class ## Objects from the Class Objects can be created by calls of the form `new("stmodelKM", ...)` or by the constructor function stmodel.KM. ## Slots - **`coltrt`:**: Object of class `"numeric"` the treatment variable - **`survTime`:**: Object of class `"numeric"` the time to event variable - **`censor`:**: Object of class `"numeric"` the censor variable - **`trts`:**: Object of class `"numeric"` a vector containing the codes for the 2 treatment groups, first and second treatment groups, respectively - **`timePoint`:**: Object of class `"numeric"` timepoint to estimate survival ## Extends Class `"stmodel"`, directly. ## Methods - **estimate**: `signature(.Object = "stmodelKM")`: estimate the effect in absolute and relative scale of the overall population and each subpopulation. - **print**: `signature(.Object = "stmodelKM")`: print the estimate, covariance matrices and statistics. - **test**: `signature(.Object = "stmodelKM")`: perform the permutation tests or GEE and obtain various statistics. ## Returns The new method returns the stmodelKM object. The estimate method returns a list with the following fields: - **model**: the stepp model - "KMe" - **sObs1**: a vector of effect estimates of all subpopulations based on the first treatment - **sSE1**: a vector of standard errors of effect estimates of all subpopulations based on the first treatment - **oObs1**: effect estimate of the entire population based on the first treatment - **oSE1**: the standard error of the effect estimate of the entire population based on the first treatment - **sObs2**: a vector of effect estimates of all subpopulations based on the group treatment - **sSE2**: a vector of standard errors of effect estimates of all subpopulations based on the first treatment - **oObs2**: effect estimate of the entire population based on the first treatment - **oSE2**: the standard error of the effect estimate of the entire population based on the first treatment - **skmw**: Wald's statistics for the effect estimate differences between the two treatments - **logHR**: a vector of log hazard ratio estimate of the subpopulations comparing first and second treatments - **logHRSE**: a vector of standard error of the log hazard ratio estimate of the subpopulations comparing first and second treatment - **ologHR**: the log hazard ratio estimate of the entire population comparing first and second treatment - **ologHRSE**: the standard error of the log hazard ratio estimate of the entire population comparing first and second treatment - **logHRw**: Wald's statistics for the log hazard ratio between the two treatment The test method returns a list with the following fields: - **model**: the stepp model - "KMt" - **sigma**: the covariance matrix for subpopulations based on effect differences - **hasigma**: the homogeneous association covariance matrix for subpopulations based on effect differences - **HRsigma**: the covariance matrix for the subpopulations based on hazard ratios - **haHRsigma**: the homogeneous association covariance matrix for subpopulations based on hazard ratios - **pvalue**: the supremum pvalue based on effect difference - **chi2pvalue**: the chisquare pvalue based on effect difference - **hapvalue**: the homogeneous association pvalue based on effect difference ## Author(s) Wai-Ki YIp ## See Also `stwin`, `stsubpop`, `stmodelCI`, `stmodelGLM`, `steppes`, `stmodel`, `stepp.win`, `stepp.subpop`, `stepp.KM`, `stepp.CI`, `stepp.GLM`, `stepp.test`, `estimate`, `generate` ## Examples ```r showClass("stmodelKM") #GENERATE TREATMENT VARIABLE: N <- 1000 Txassign <- sample(c(1,2), N, replace=TRUE, prob=c(1/2, 1/2)) n1 <- length(Txassign[Txassign==1]) n2 <- N - n1 #GENERATE A COVARIATE: covariate <- rnorm(N, 55, 7) #GENERATE SURVIVAL AND CENSORING VARIABLES ASSUMING A TREATMENT COVARIATE INTERACTION: Entry <- sort( runif(N, 0, 5) ) SurvT1 <- .5 beta0 <- -65 / 75 beta1 <- 2 / 75 Surv <- rep(0, N) lambda1 <- -log(SurvT1) / 4 Surv[Txassign==1] <- rexp(n1, lambda1) Surv[Txassign==2] <- rexp(n2, (lambda1*(beta0+beta1*covariate[Txassign==2]))) EventTimes <- rep(0, N) EventTimes <- Entry + Surv censor <- rep(0, N) time <- rep(0,N) for ( i in 1:N ) { censor[i] <- ifelse( EventTimes[i] <= 7, 1, 0 ) time[i] <- ifelse( EventTimes[i] < 7, Surv[i], 7 - Entry[i] ) } modKM <- new("stmodelKM", coltrt=Txassign, survTime=time, censor=censor, trts=c(1,2), timePoint=4) ```

stmodelKM-class function