

   PPaacckkaaggee aa ssuurrvviivvaall vvaarriiaabbllee

        Surv(time, event)  or Surv(time, time2, event)

   AArrgguummeennttss::

       time: for right censored data, this is the follow up
             time.  For interval data, the first argument is
             the starting time for the interval.

      event: The status indicator, normally 0=alive, 1=dead.
             Other choices are T/F (TRUE = death) or 1/2
             (2=death).  For interval censored data, the status
             indicator is 0=right censored, 1= event at `time',
             2=left censored, 3=interval censored.

      time2: For interval censored  or counting process data
             only, the ending time of the interval.  Intervals
             are assummed to be open on the left and closed on
             the right, (start, end].  For counting process
             data, `event' marks whether an event occured at
             the end of the interval.

       type: one of left, right, counting, interval, or inter-
             val2.  If this is not specified, the default is
             either right or counting, depending on whether the
             `time2' argument is absent or present, respec-
             tively.

     origin: for counting process data, the hazard function
             origin.  This is most often used in conjunction
             with a model containing time dependent strata in
             order to align the subjects properly when they
             cross over from one strata to another.

   VVaalluuee::

        An object of class 'Surv'.  There are methods for
        `print', `is.na', and subscripting survival objects.
        To include a survival object inside a data frame, use
        the `I()' function.  Surv objects are implimented as a
        matrix of 2 or 3 columns.

   MMEETTHHOODD::

        In theory it is possible to represent interval censored
        data without a third column containing the explicit
        status.  Exact, right censored, left censored and
        interval censored observation would be represented as
        intervals of (a,a), (a, infinity), (-infinity,b), and
        (a,b) respectively; each specifing the interval within
        which the event is known to have occured.  Infinity is,
        of course, impractical in a computer routine.  If
        `type' is "interval2" then the representation given
        above is assumed, with NA taking the place of infinity.
        If type='interval' then an explicit status code must be
        given in the third argument.  If the status code is 0,
        1 or 2, then the relevant information is assumed to be
        contained in `time',  the value in `time2' is ignored,
        and the second column of the result will contain a
        placeholder.  At present, all of the methods that han-
        dle interval censored data are parametric models, so
        the distinction between open and closed intervals is
        unimportant.  The distinction is important for counting
        process data and the Cox model.

   EExxaammpplleess::

        Surv(aml$time, aml$status)
         [1] 9    13   13+  18   23   28+  31   34   45+  48   161+ 5    5    8    8
         [16] 12   16+  23   27   30   33   43   45

