survfit.object: Survival Curve Object

In therneau/survival: Survival Analysis

Survival Curve Object

Description

This class of objects is returned by the survfit class of functions to represent a fitted survival curve. For a multi-state model the object has class c('survfitms', 'survfit').

Objects of this class have methods for the functions print, summary, plot, points and lines. The print.survfit method does more computation than is typical for a print method and is documented on a separate page.

Arguments

n	total number of observations in each curve.
time	the time points at which the curve has a step.
n.risk	the number of subjects at risk at t.
n.event	the number of events that occur at time t.
n.enter	for counting process data only, and only if there was an id argument, the number of subjects that enter the risk set during the current interval. If there are event/censoring times at 1, 3, 5 for instance, someone who enters at time 1 is counted in the (1, 3] interval, i.e., appears in the row for time 3.
n.censor	for counting process data only, the number of subjects who exit the risk set, without an event, at time t. (For right censored data, this number can be computed from the successive values of the number at risk).
surv	the estimate of survival at time t+0. This may be a vector or a matrix. The latter occurs when a set of survival curves is created from a single Cox model, in which case there is one column for each covariate set.
pstate	a multi-state survival will have the pstate component instead of surv. It will be a matrix containing the estimated probability of each state at each time, one column per state.
std.err	for a survival curve this contains standard error of the cumulative hazard or -log(survival), for a multi-state curve it contains the standard error of prev. This difference is a reflection of the fact that each is the natural calculation for that case.
cumhaz	optional. Contains the cumulative hazard for each possible transition.
counts	optional. If weights were used, the n.risk etc elements contain weighted sums; the counts matrix will contain unweighted values. Weighted values are normally more useful for further computation, unweighted may be preferred for labeling or printout.
strata	if there are multiple curves, this component gives the number of elements of the time vector corresponding to the first curve, the second curve, and so on. The names of the elements are labels for the curves.
upper	optional upper confidence limit for the survival curve or pstate
lower	options lower confidence limit for the survival curve or pstate
t0	for multi-state curves,the starting time for the curve
p0, sp0	for a multistate object, the distribution of starting states. If the curve has a strata dimension, this will be a matrix one row per stratum. The sp0 element has the standard error of p0, if p0 was estimated.
newdata	for survival curves from a fitted model, this contains the covariate values for the curves
n.id	the total number of unique id values that contributed to the curve. This is only available if the original call used the id option.
conf.type	the approximation used to compute the confidence limits.
conf.int	the level of the confidence limits, e.g. 90 or 95%.
transitions	for multi-state data, the total number of transitions of each type.
na.action	the returned value from the na.action function, if any. It will be used in the printout of the curve, e.g., the number of observations deleted due to missing values.
call	an image of the call that produced the object.
type	type of survival censoring.
influence.p, influence.c	optional influence matrices for the pstate (or surv) and for the cumhaz estimates. A list with one element per stratum, each element of the list is an array indexed by subject, time, state.
version	the version of the object. Will be missing, 2, or 3

Structure

The following components must be included in a legitimate survfit or survfitms object.

Subscripts

Survfit objects can be subscripted. This is often used to plot a subset of the curves, for instance. From the user's point of view the survfit object appears to be a vector, matrix, or array of curves. The first dimension is always the underlying number of curves or “strata”; for multi-state models the state is always the last dimension. Predicted curves from a Cox model can have a second dimension which is the number of different covariate prediction vectors.

Details

The survfit object has evolved over time: when first created there was no thought of multi-state models for instance. This evolution has almost entirely been accomplished by the addition of new elements.

For both plots of the curves and computation of the restricted mean time in state (RMTS) we need the concept of a starting point t0 and starting prevalence of the states p0 for each curve. (Sojourn time, area under the curve and restricted mean survival time are other labels for the RMTS). Time 0 is not, by default, included as part of the standard tableau of results, i.e., time, number at risk, number of events, etc. For simple survival with a 0/1 status variable, the starting state p0 is the obvious default of "everyone alive", and t0 is formally not discernable from the data and so was left out. (A design decision made in 1986, and now far too late to change.) However, for plots t0 is assumed to be the minimum of 0 and all observed times. Negative survival times are unusual but not invalid. Multi-state survival curves include t0 and p0 as a part of the returned object. The first is a single value for all curves, the second is per curve.

The survfit0 routine can be used to add these values to the main curve data, this is done by the default print, plot, and summary methods for survfit objects. The methods vignette has discussion of the rationale of how t0 and p0 are chosen in the multi-state case. Notice that if there is an event at time t0, e.g., a death on day 0 for competing risks, then p0 will contain the prevalence just before that event occured.

See Also

plot.survfit, summary.survfit, print.survfit, survfit, survfit0

therneau/survival documentation built on April 26, 2024, 8:23 a.m.