R/prep.data.event.AJ.R

In TP.idm: Estimation of Transition Probabilities for the Illness-Death Model

prep.data.event.AJ <-
function(dNs, Ys, sum_dNs,states, tr.states) {
    
    split_dNs <- strsplit(colnames(dNs), "  ") ## string splits names
    split_Ys <- strsplit(colnames(Ys), " ")  ## string split names of Ys
    split_sum_dNs <- strsplit(colnames(sum_dNs), " ") ## string splits names of dNs
    
    ## reducing dNs & Ys to just event times & noncens states
    ##  looks at noncensored columns
    event.dNs.id <- which(sapply(split_dNs, function(x) x[1]=="tr 1 2" | x[1]=="tr 1 3" | x[1]=="tr 2 3"))
    ## identifies times where transitions occur
    event.row.id <- which(apply(dNs[, event.dNs.id, drop=FALSE], 1, function(x) any(x>0)))
    dNs.event <- dNs[event.row.id, event.dNs.id, drop=FALSE] ## reduces dNs  
    
    tr.states.event <- names(which(sapply(tr.states, function(x) length(x)>0))) 
    event.Ys.id <- which(sapply(split_Ys, function(x) x[2]%in%tr.states.event)) 
    Ys.event <- Ys[event.row.id, event.Ys.id, drop=FALSE] ## reduces Ys
    
    event.sum_dNs.id <- which(sapply(split_sum_dNs, function(x) x[2]%in%states))
    sum_dNs.event <- sum_dNs[event.row.id, event.sum_dNs.id, drop=FALSE]
    
    ans <- list(dNs=dNs.event, Ys=Ys.event, sum_dNs=sum_dNs.event)
    return(ans)
    
  }