Create a survival object, usually used as a response variable in a model formula. Argument matching is special for this function, see Details below.
Surv(time, time2, event,
type=c('right', 'left', 'interval', 'counting', 'interval2', 'mstate'),
origin=0)
is.Surv(x)An object of class Surv. There are methods for print,
is.na, and subscripting survival objects. Surv objects
are implemented as a matrix of 2 or 3 columns that has further
attributes. These include the type (left censored, right censored,
counting process, etc.) and labels for the states for multi-state
objects. Any attributes of the input arguments are also preserved
in inputAttributes. This may be useful for other packages that
have attached further information to data items such as labels; none
of the routines in the survival package make use of these
values, however.
In the case of is.Surv, a logical value TRUE if x
inherits from class "Surv", otherwise an FALSE.
for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval.
The status indicator, normally 0=alive, 1=dead. Other choices are
TRUE/FALSE (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.
For multiple endpoint data the event variable will be a factor,
whose first level is treated as censoring.
Although unusual, the event indicator can be omitted, in which case
all subjects are assumed to have an event.
ending time of the interval for interval censored or counting
process data only. Intervals are assumed to be open on the left and
closed on the right, (start, end]. For counting process
data, event indicates whether an event occurred at the end of
the interval.
character string specifying the type of censoring. Possible values
are "right", "left", "counting",
"interval", "interval2" or "mstate".
for counting process data, the hazard function origin. This option was intended to be 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, but it has rarely proven useful.
any R object.
When the type argument is missing the code assumes a type based
on the following rules:
If there are two unnamed arguments, they will match time and
event in that order. If there are three unnamed arguments
they match time, time2 and event.
If the event variable is a factor then type mstate is
assumed. Otherwise type right if there is no time2
argument, and type counting if there is.
As a consequence the type argument will normally be omitted.
When the survival type is "mstate" then the status variable will be
treated as a factor. The first level of the factor is taken to
represent censoring and remaining ones a transition to the given
state. (If the status variable is a factor then mstate is assumed.)
Interval censored data can be represented in two ways. For the first
use type = "interval" and the codes shown above. In that usage the
value of the time2 argument is ignored unless event=3.
The second approach is to think of each observation as a time
interval with (-infinity, t2) for left censored, (t1, infinity) for
right censored, (t,t) for exact and (t1, t2) for an interval.
This is the approach used for type = interval2. Infinite values can
be represented either by actual infinity (Inf) or NA.
The second form has proven to be the more useful one.
Presently, the only methods allowing interval censored data are the
parametric models computed by survreg and survival curves
computed by survfit; for both of these,
the distinction between open and closed intervals
is unimportant.
The distinction is important for counting process data and
the Cox model.
The function tries to distinguish between the use of 0/1 and 1/2 coding for
censored data via the condition
if (max(status)==2).
If 1/2 coding is used and all the subjects are censored, it will
guess wrong.
In any questionable case it is safer to use logical coding,
e.g., Surv(time, status==3) would indicate that '3' is
the code for an event.
For multi-state survival the status variable will be a factor, whose
first level is assumed to correspond to censoring.
Surv objects can be subscripted either as a vector, e.g.
x[1:3] using a single subscript,
in which case the drop argument is ignored and the result will be
a survival object;
or as a matrix by using two subscripts.
If the second subscript is missing and drop=F
(the default),
the result of the subscripting will be a Surv object, e.g.,
x[1:3,,drop=F],
otherwise the result will be a matrix (or vector), in accordance with
the default behavior for subscripting matrices.
coxph,
survfit,
survreg, lung.
with(aml, Surv(time, status))
survfit(Surv(time, status) ~ ph.ecog, data=lung)
Surv(heart$start, heart$stop, heart$event)
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