# Simulated true hazard (bootstrap average) Compute the Monte Carlo / bootstrap averaged alpha (true hazard) across a list of datasets. ```r Sim.True.Hazard(data.use, id, size_s_grid, marker_name1 = marker_name1, marker_name2 = marker_name2, event_time_name = event_time_name, time_name = time_name, event_name = event_name, in.par, b) ``` ## Arguments - `data.use`: List of data.frames (bootstrap samples). - `id`: Id column name. - `size_s_grid`: user supplied grid length on the time_name argument - `marker_name1`: First marker name. - `marker_name2`: Second marker name. - `event_time_name`: Name of event time column. - `time_name`: Name of observation time column. - `event_name`: Name of event indicator column. - `in.par`: Numeric indexing parameters. - `b`: Bandwidth. ## Returns Numeric vector (row means of alpha estimates). ## Examples ```r marker_name1 <- 'albumin' marker_name2 <- 'serBilir' event_time_name <- 'years' time_name <- 'year' event_name <- 'status2' id<-'id' ls<-50 #user supplied grid length on the time_name argument par.x1 <- 0.0702 #indexing parameter for marker 1 par.x2 <- 0.0856 #indexing parameter for marker 2 t.x1 = 0 # conditioning level for marker 1, assumes zero mean variable t.x2 = 1.9 # conditioning level for marker 2, assumes zero mean variable b = 0.42 # The result, on the indexed marker 'indmar' of #\code{b_selection(pbc2,'indmar','years','year','status2',I=26,seq(0.2,0.4,by=0.01))} t = par.x1 * t.x1 + par.x2 *t.x2 xin <- pbc2[,c(id, marker_name1, marker_name2, event_time_name, time_name, event_name)] n <- length(xin$id) nn<-max( as.double(xin[,'id']) ) # Create bootstrap samples by group set.seed(1) B<-100 #set bootstrap iterations here Boot.samples<-list() for(j in 1:B) { i.use<-c() id.use<-c() index.nn <- sample (nn, replace = TRUE) for(l in 1:nn) { i.use2<-which(xin[,id]==index.nn[l]) i.use<-c(i.use, i.use2) id.use2<-rep(index.nn[l], times=length(i.use2)) id.use<-c(id.use, id.use2) } xin.i<-xin[i.use,] xin.i<-xin[i.use,] Boot.samples[[j]]<- xin.i[order(xin.i$id),] } #compute the bootstrap simulated true HR function: true.hazard<- Sim.True.Hazard(Boot.samples, id='id', size_s_grid=ls, marker_name1=marker_name1, marker_name2= marker_name2, event_time_name = event_time_name, time_name = time_name, event_name = event_name, in.par = c(par.x1, par.x2), b) ```

Sim.True.Hazard function