# Class `"stmodelCI"` This is the stepp model of survival data with competing risks. 1.1 class ## Objects from the Class Objects can be created by calls of the form `new("stmodelCI", ...)` or by the constructor function stepp.CI. ## Slots - **`coltrt`:**: Object of class `"numeric"` the treatment variable - **`coltime`:**: Object of class `"numeric"` the time to event variable - **`coltype`:**: Object of class `"numeric"` variable with distinct codes for different causes of failure where coltype=0 for censored observations; coltype=1 for event of interest; coltype=2 for other causes of failure - **`trts`:**: Object of class `"numeric"` a vector containing the codes for the 2 treatment groups, 1st and 2nd treatment groups, respectively - **`timePoint`:**: Object of class `"numeric"` timepoint to estimate survival ## Extends Class `"stmodel"`, directly. ## Methods - **estimate**: `signature(.Object = "stmodelCI")`: estimate the effect in absolute and relative scale of the overall and each subpopulation - **print**: `signature(.Object = "stmodelCI")`: print the estimate, covariance matrices and statistics - **test**: `signature(.Object = "stmodelCI")`: perform the permutation tests or GEE and obtain various statistics ## Returns The new method returns the stmodelCI object. The estimate method returns a list with the following fields: - **model**: the stepp model - "CIe" - **sObs1**: a vector of effect estimates of all subpopulations based on the 1st treatment - **sSE1**: a vector of standard errors of effect estimates of all subpopulations based on the 1st treatment - **oObs1**: effect estimate of the entire population based on the 1st treatment - **oSE1**: the standard error of the effect estimate of the entire population based on the 1st treatment - **sObs2**: a vector of effect estimates of all subpopulations based on the 1st treatment - **sSE2**: a vector of standard errors of effect estimates of all subpopulations based on the 1st treatment - **oObs2**: effect estimate of the entire population based on the 1st treatment - **oSE2**: the standard error of the effect estimate of the entire population based on the 1st 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 1st and 2nd treatments - **logHRSE**: a vector of standard error of the log hazard ratio estimate of the subpopulations comparing 1st and 2nd treatments - **ologHR**: the log hazard ratio estimate of the entire population comparing 1st and 2nd treatments - **ologHRSE**: the standard error of the log hazard ratio estimate of the entire population comparing 1st and 2nd treatments - **logHRw**: Wald's statistics for the log hazard ratio between the two treatments The test method returns a list with the following fields: - **model**: the stepp model - "CIt" - **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 ratio - **haHRsigma**: the homogeneous association covariance matrix for subpopulations based on hazard ratio - **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 - **HRpvalue**: the supremum pvalue based on hazard ratio - **haHRpvalue**: the homogeneous association pvalue based on hazard ratio ## Author(s) Wai-Ki Yip ## See Also `stwin`, `stsubpop`, `stmodelKM`, `stmodelCI`, `stmodelGLM`, `steppes`, `stmodel`, `stepp.win`, `stepp.subpop`, `stepp.KM`, `stepp.GLM`, `stepp.test`, `estimate`, `generate` ## Examples ```r showClass("stmodelCI") ## n <- 1000 # set the sample size mu <- 0 # set the mean and sd of the covariate sigma <- 1 beta0 <- log(-log(0.5)) # set the intercept for the log hazard beta1 <- -0.2 # set the slope on the covariate beta2 <- 0.5 # set the slope on the treatment indicator beta3 <- 0.7 # set the slope on the interaction prob2 <- 0.2 # set the proportion type 2 events cprob <- 0.3 # set the proportion censored set.seed(7775432) # set the random number seed covariate <- rnorm(n,mean=mu,sd=sigma) # generate the covariate values Txassign <- rbinom(n,1,0.5) # generate the treatment indicator x3 <- covariate*Txassign # compute interaction term # compute the hazard for type 1 event lambda1 <- exp(beta0+beta1*covariate+beta2*Txassign+beta3*x3) lambda2 <- prob2*lambda1/(1-prob2) # compute the hazard for the type 2 event # compute the hazard for censoring time lambda0 <- cprob*(lambda1+lambda2)/(1-cprob) t1 <- rexp(n,rate=lambda1) # generate the survival time for type 1 event t2 <- rexp(n,rate=lambda2) # generate the survival time for type 2 event t0 <- rexp(n,rate=lambda0) # generate the censoring time time <- pmin(t0,t1,t2) # compute the observed survival time type <- rep(0,n) type[(t1 < t0)&(t1 < t2)] <- 1 type[(t2 < t0)&(t2 < t1)] <- 2 # create the stepp model object to analyze the data using Cumulative Incidence approach x <- new ("stmodelCI", coltrt=Txassign, trts=c(0,1), coltime=time, coltype=type, timePoint=1.0) ```

stmodelCI-class function