eha (version 1.0)

toBinary: Transforms a "survival" data frame into a data frame suitable for binary (logistic) regression

Description

The result of the transformation can be used to do survival analysis via logistic regression. If the cloglog link is used, this corresponds to a discrete time analogue to Cox's proportional hazards model.

Usage

toBinary(dat, surv = c("enter", "exit", "event"),
strats, max.survs = NROW(dat))

Arguments

dat

A data frame with three variables representing the survival response. The default is that they are named enter, exit, and event

surv

A character string with the names of the three variables representing survival.

strats

An eventual stratification variable.

max.survs

Maximal numger of survivors per risk set. If set to a (small) number, survivors are sampled from the risk sets.

Value

• Returns a data frame expanded risk set by risk set. The three "survival variables" are replaced by a variable named event (which overwrites an eventual variable by that name in the input). Two more variables are created, riskset and orig.row.
• event
  Indicates an event in the corresponding risk set.
• riskset
  Factor (with levels 1, 2, ...) indicating risk set.
• orig.row
  The row number for this item in the original data frame.

Details

toBinary calls risksets in the eha package.

References

~put references to the literature/web site here ~

See Also

mlreg

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function(dat,
                     surv = c("enter", "exit", "event"),
                     strats,
                     max.survs = NROW(dat)) 
{
    if (!is.data.frame(dat))
      stop("dat must be a data frame")
    if (length(surv) != 3)
      stop("surv must have length 3")
    fixed.names <- names(dat)
    surv.indices <- match(surv, fixed.names)
    if (length(which(is.na(surv.indices)))) {
        x <- which(is.na(surv.indices))
        stop(paste(surv[x], "is not a name in the data frame."))
    }
    enter <- dat[, surv.indices[1]]
    exit <- dat[, surv.indices[2]]
    event <- dat[, surv.indices[3]]

    covars <- dat[, -surv.indices, drop = FALSE]

    nn <- NROW(dat)

    if (missing(strats) || is.null(strats)) strats <- rep(1, nn)
    rs <- risksets(Surv(enter, exit, event), strata = strats, max.survs)

    weg <- (abs(rs$size - rs$n.events) > 0.01)
    rs$riskset <- rs$riskset[rep(weg, rs$size)]
    rs$eventset <- rs$eventset[rep(weg, rs$n.events)]
    rs$n.events <- rs$n.events[weg]
    rs$size <- rs$size[weg]

    n.rs <- length(rs$size)
    ev <- numeric(sum(rs$size))
    start <- 1
    for (i in 1:n.rs) {
        ev[start:(start + rs$n.events[i] - 1)] <- 1
        start <- start + rs$size[i]
    }
    rs$ev <- ev

    out <- data.frame(event = rs$ev,
                      riskset = factor(rep(1:length(rs$size), rs$size))
                      )
                      
    out <- cbind(out, covars[rs$riskset, , drop = FALSE])
    out$orig.row <- (1:nn)[rs$riskset]
    out
  }