survival (version 2.35-3)

survfit.coxph: Compute a Survival Curve from a Cox model

Description

Computes the predicted survivor function for a Cox proportional hazards model.

Usage

## S3 method for class 'coxph':
survfit(formula, newdata, 
        se.fit=TRUE, conf.int=.95,
        individual=FALSE, 
        type,vartype,
        conf.type=c("log","log-log","plain","none"),...)

Arguments

formula

A coxph object.

newdata

a data frame with the same variable names as those that appear in the coxph formula. The curve(s) produced will be representative of a cohort whose covariates correspond to the values in newdata. Default

individual

a logical value indicating whether each row of newdata represents a distinct individual (FALSE, the default), or if each row of the data frame represents different time epochs for only one individual (TRUE). In the former

conf.int

the level for a two-sided confidence interval on the survival curve(s). Default is 0.95.

se.fit

a logical value indicating whether standard errors should be computed. Default is TRUE.

type,vartype

a character string specifying the type of survival curve. Possible values are "aalen" or "kaplan-meier" (only the first two characters are necessary). The default is "aalen" Earlier versions of

conf.type

One of "none", "plain", "log" (the default), or "log-log". Only enough of the string to uniquely identify it is necessary. The first option causes confidence intervals not to be generate

...

for future methods

Value

• an object of class "survfit". See survfit.object for details. Methods defined for survfit objects are print, plot, lines, and points.

References

Dorey, F. J. and Korn, E. L. (1987). Effective sample sizes for confidence intervals for survival probabilities. Statistics in Medicine 6, 679-87.

Fleming, T. H. and Harrington, D. P. (1984). Nonparametric estimation of the survival distribution in censored data. Comm. in Statistics 13, 2469-86.

Kablfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. New York:Wiley.

Link, C. L. (1984). Confidence intervals for the survival function using Cox's proportional hazards model with covariates. Biometrics 40, 601-610.

Tsiatis, A. (1981). A large sample study of the estimate for the integrated hazard function in Cox's regression model for survival data. Annals of Statistics 9, 93-108.

Details

See survfit for more Details. The estimates used are the Kalbfleisch-Prentice (Kalbfleisch and Prentice, 1980, p.86) and the Tsiatis/Link/Breslow, which reduce to the Kaplan-Meier and Fleming-Harrington estimates, respectively, when the weights are unity. When the parent data is a Cox model, there is an extra term in the variance of the curve, due to the variance of the coefficients and hence variance in the computed weights. Details of the Aalen estimator and its variance are found in Tsiatis (1981).

The two variants of the F-H estimate have to do with how ties are handled. If there were 3 deaths out of 10 at risk, then the first increments the hazard by 3/10 and the second by 1/10 + 1/9 + 1/8. For the first method S(t) = exp(H), where H is the Nelson-Aalen cumulative hazard estimate, whereas the fh2 method will give results S(t) results closer to the Kaplan-Meier. For curves created after a Cox model these correspond to the Breslow and Efron estimates, respectively, and the proper choice is made automatically.

See Also

print, plot, lines, coxph, Surv, strata.

Examples

#fit a Kaplan-Meier and plot it 
fit <- survfit(Surv(time, status) ~ x, data = aml) 
plot(fit, lty = 2:3) 
legend(100, .8, c("Maintained", "Nonmaintained"), lty = 2:3) 

#fit a Cox proportional hazards model and plot the  
#predicted survival for a 60 year old 
fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian) 
plot(survfit(fit, newdata=data.frame(age=60)),
     xscale=365.25, xlab = "Years", ylab="Survival") 

# Here is the data set from Turnbull
#  There are no interval censored subjects, only left-censored (status=3),
#  right-censored (status 0) and observed events (status 1)
#
#                             Time
#                         1    2   3   4
# Type of observation
#           death        12    6   2   3
#          losses         3    2   0   3
#      late entry         2    4   2   5
#
tdata <- data.frame(time  =c(1,1,1,2,2,2,3,3,3,4,4,4),
                    status=rep(c(1,0,2),4),
                    n     =c(12,3,2,6,2,4,2,0,2,3,3,5))
fit  <- survfit(Surv(time, time, status, type='interval') ~1, 
              data=tdata, weight=n)

#
# Time to progression/death for patients with monoclonal gammopathy
#  Competing risk curves (cumulative incidence)
fit1 <- survfit(Surv(stop, event=='progression') ~1, data=mgus1,
                    subset=(start==0))
fit2 <- survfit(Surv(stop, status) ~1, data=mgus1,
                    subset=(start==0), etype=event) #competing risks
# CI curves are always plotted from 0 upwards, rather than 1 down
plot(fit2, fun='event', xscale=365.25, xmax=7300, mark.time=FALSE,
            col=2:3, xlab="Years post diagnosis of MGUS")
lines(fit1, fun='event', xscale=365.25, xmax=7300, mark.time=FALSE,
            conf.int=FALSE)
text(10, .4, "Competing Risk: death", col=3)
text(16, .15,"Competing Risk: progression", col=2)
text(15, .30,"KM:prog")